US20020176113A1 - Dynamic image correction and imaging systems - Google Patents
Dynamic image correction and imaging systems Download PDFInfo
- Publication number
- US20020176113A1 US20020176113A1 US09/960,276 US96027601A US2002176113A1 US 20020176113 A1 US20020176113 A1 US 20020176113A1 US 96027601 A US96027601 A US 96027601A US 2002176113 A1 US2002176113 A1 US 2002176113A1
- Authority
- US
- United States
- Prior art keywords
- image
- pixel
- mask
- original
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000003384 imaging method Methods 0.000 title claims description 7
- 238000003702 image correction Methods 0.000 title description 9
- 238000000034 method Methods 0.000 claims abstract description 94
- 230000008569 process Effects 0.000 claims description 17
- 238000012935 Averaging Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 12
- 230000001965 increasing effect Effects 0.000 claims description 8
- 238000000354 decomposition reaction Methods 0.000 claims description 7
- 230000002708 enhancing effect Effects 0.000 claims description 2
- 238000002595 magnetic resonance imaging Methods 0.000 claims description 2
- 238000007620 mathematical function Methods 0.000 claims 4
- 230000008901 benefit Effects 0.000 abstract description 6
- 238000004422 calculation algorithm Methods 0.000 description 25
- 238000012545 processing Methods 0.000 description 18
- 230000006870 function Effects 0.000 description 16
- 238000007726 management method Methods 0.000 description 15
- 238000004364 calculation method Methods 0.000 description 11
- 239000003086 colorant Substances 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 238000011946 reduction process Methods 0.000 description 8
- 230000037331 wrinkle reduction Effects 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 238000007796 conventional method Methods 0.000 description 4
- 238000012937 correction Methods 0.000 description 4
- 230000010365 information processing Effects 0.000 description 4
- 230000003247 decreasing effect Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 244000235115 Alocasia x amazonica Species 0.000 description 1
- 238000001444 catalytic combustion detection Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000009189 diving Effects 0.000 description 1
- 206010016256 fatigue Diseases 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 229910044991 metal oxide Inorganic materials 0.000 description 1
- 150000004706 metal oxides Chemical class 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000135 prohibitive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000010076 replication Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000005488 sandblasting Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G06T5/75—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration by the use of local operators
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/40—Picture signal circuits
- H04N1/407—Control or modification of tonal gradation or of extreme levels, e.g. background level
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/40—Picture signal circuits
- H04N1/409—Edge or detail enhancement; Noise or error suppression
- H04N1/4092—Edge or detail enhancement
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
Description
- This application claims the benefit of the following U.S. Provisional Patent Applications: Serial No. 60/234,520, filed on Sep. 21, 2000, and entitled “Method of Generating an Image Mask for Improving Image Detail;” Serial No. 60/234,408, filed on Sep. 21, 2000, and entitled “Method of Applying An Image Mask For Improving Image Detail;” and Serial No. 60/285,591, filed on Apr. 19, 2001, and entitled “Method and System and Software for Applying an Image Mask for Improving Image Detail;” of common assignee herewith.
- The present invention relates generally to imaging systems and image processing and more particularly to dynamic image correction and imaging systems.
- A variety of methods are commonly employed to capture an image. For example, photographic film may be exposed to light reflected from a desired subject to record a latent image withing the film. The film is then developed to generate a “negative” or “positive” from which prints or transparencies can be made and delivered to consumers. The negative, positive, or print can be scanned to produce a digital representation of the subject. Alternately, digital devices such as digital camera, video recorder, and the like, may be used to directly capture a digital representation of the desired subject by measuring the reflected light from the subject.
- Lighting is particularly important when capturing images and care is often take to ensure the proper lighting of the subject matter of the image. If too much light is reflected from the subject, the captured image will be over-exposed, and the final image will appear washed-out. If too little light, the captured image will appear under-exposed, and the final image will appear dark. Similarly, if the proper lighting is not provided from a proper angle, for example when one part of an image is in bright light while another part is in shadow, some of the image might be properly exposed, while the remainder of the image is either under-exposed or over-exposed. Conventional digital devices are particularly prone to having over-exposed and under-exposed portions of an image.
- If during an image capture process the subject is over-exposed or under-exposed, the mistake can sometimes be minimized in the processing (or development) and/or printing process. Typically, when an image is captured on film, the negative contains much more image detail than can be reproduced in a photographic print, and so a photographic print includes only a portion of the information available to be printed. Similarly, images captured directly by digital devices often have considerably more image detail then can be reproduced or output. By choosing the proper portion of the image detail to print, the final processed image may be compensated for the mistakes made during image capture. However, particularly in the case in which some areas of an image are underexposed and other areas of an image are over-exposed, it is difficult to correct both the under-exposed and over-exposed portions of the image.
- Conventional correction techniques for reducing the effects of over-exposed and under-exposed regions are generally performed by hand and can be extremely expensive. One conventional correction technique is to apply a cutout filter. In this technique, the image is divided into large, homogeneous regions, and a filter is applied to each of these regions. Referring now to FIG. 1, in which a
conventional cutout filter 110 is shown. The original image is of a castle. Assume that in the original image, thesky 160 lacks detail and is washed out, while thecastle 120 is in shadow. Thecutout filter 110 has adark sky 160 and alight castle 120, so that when applied to the original image, thesky 160 in the resultant image will be darker, and thecastle 120 will be lighter, thereby improving “gross” image detail. - A drawback of
cutout filter 110 is that image detail within the regions is not properly corrected unless the selected region is truly homogeneous, which is not very likely. As a result, detail within each region is lost. The number of regions selected for filtering may be increased, but selecting more regions greatly increases the time and labor needed to generate thecutout filter 110. In addition, this technique and other conventional techniques tends to create visually unappealing boundaries between the regions. - In accordance with one implementation of the present invention a method of enhancing an image is provided. In one embodiment, the method comprises obtaining an image mask of the original image. The image mask and the original image each comprise a plurality of pixels having varying values. The plurality of mask pixels are set to form sharper edges corresponding to areas of more rapidly changing pixel values in the original image. The pixels are further arranged to form areas of less sharp regions corresponding to areas of less rapidly changing pixel values in the original image. The method further comprises combining the image mask with the original image to obtain a masked image.
- Another embodiment of the present invention provides for a digital file tangibly embodied in a computer readable medium. The digital file is generated by implementing a method comprising obtaining an image mask of an original image. The image mask and the original image each comprise a plurality of pixels having varying values. The plurality of mask pixels are set to form sharper edges corresponding to areas of more rapidly changing pixel values in the original image. The pixels are further arranged to form areas of less sharp regions corresponding to areas of less rapidly changing pixel values in the original image. The method further comprises combining the image mask with the original image to obtain a masked image.
- An additional embodiment of the present invention provides for a computer readable medium tangibly embodying a program of instructions. The program of instructions is capable of obtaining an image mask of an original image. The image mask and the original image each comprise a plurality of pixels having varying values. The plurality of mask pixels are set to form sharper edges corresponding to areas of more rapidly changing pixel values in the original image. The pixels are further arranged to form areas of less sharp regions corresponding to areas of less rapidly changing pixel values in the original image. The program of instructions is further capable of combining the image mask with the original image to obtain a masked image.
- Yet another embodiment of the present invention provides for a system comprising an image sensor to convert light reflected from an image into information representative of the image, a processor, memory operably coupled to the processor, and a program of instructions capable of being store in the memory and executed by the processor. The program of instructions manipulate the processor to obtain an image mask, the image mask and the information representative of the image each including a plurality of pixels having varying values, wherein the values of the plurality of mask pixels are set to form sharper edges corresponding to areas of more rapidly changing pixel values in the original image and less sharp regions corresponding to areas of less rapidly changing pixel values in the original image. The program of instructions also manipulate the processor to combine the image mask with the information representative of the image to obtain a masked image.
- An advantage of at least one embodiment of the present invention is that an image to improve reproducible detail can be generated without user intervention.
- An additional advantage of at least one embodiment of the present invention is that an image mask can be automatically applied to an original image to generate an image with improved image detail within a reproducible dynamic range due to the image detail preserved in the image mask.
- Yet another advantage of at least one embodiment of the present invention is that calculations to improve the image detail in scanned images can be performed relatively quickly, due to a lower processing overhead and less user intervention than conventional methods.
- Other objects, advantages, features and characteristics of the present invention, as well as methods, operation and functions of related elements of structure, and the combination of parts and economies of manufacture, will become apparent upon consideration of the following description and claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures, and wherein:
- FIG. 1 is an illustration showing a conventional cutout filter;
- FIG. 2 is a block diagram illustrating a method for dynamic image correction according to one embodiment of the present invention;
- FIG. 3 is a block diagram of an original image and a dynamic image mask according to one embodiment of the present invention;
- FIG. 4 is a set of graphs showing intensity values of pixels around an edge before and after a blurring algorithm has been applied according to one embodiment of the present invention;
- FIG. 5 is a block diagram of a method for generating a dynamic image mask according to at least one embodiment of the present invention;
- FIG. 6 is a representation of an dynamic image mask with properties according to at least one embodiment of the present invention;
- FIG. 7 is a block diagram illustrating a method of applying a dynamic image mask to an image according to at least one embodiment of the present invention;
- FIG. 8A is a block diagram illustrating a wrinkle reduction process in accordance with one embodiment of the invention;
- FIG. 8B-1 is a picture illustrating an original image;
- FIG. 8B-2 is a picture illustrating the image of 8B-1 with the wrinkle reduction process applied;
- FIG. 9 is a block diagram illustrating an image capture system according to at least one embodiment of the present invention; and
- FIG. 10 is a chart illustrating improvements in the dynamic range of various image representations according to at least one embodiment of the present invention.
- FIGS.2-9 illustrate a method for dynamic image correction and imaging systems having enhanced images. As described in greater detail below, one embodiment of dynamic image correction utilizes a dynamic image mask that uses a blurring algorithm that maintains sharp boundaries of the image. The dynamic image mask is then applied to the image. In some implementations, the dynamic image mask is used to increase the amount of reproducible detail within an image. In another implementation, the dynamic image mask is used to suppress median frequencies and maintain sharp boundaries. In this implementation, the dynamic image mask can be regionally applied using an electronic brush. In yet other implementations, various embodiments of the dynamic image mask can be used as a correction map for other correction and enhancement functions. Systems for utilizing digital image correction can include a variety of image capturing or processing systems, such as digital cameras, video cameras, scanners, image processing software, and the like.
- Referring now to FIG. 2, one method of dynamic image correction200 is described. In this embodiment, dynamic image correction 200 includes creating a dynamic image mask B from an original image A. The dynamic image mask B be is then combined with original image A to generate an enhanced image C. In one embodiment, the enhanced image C has improved image detail over original image A, within a reproducible dynamic range. For example, original image A may contain detail which may not be appropriately represented when output for display or printing, such as containing high contrast over-exposed (bright) regions and under-exposed (shadow) regions. It would be helpful to brighten the detail in the shadow regions and decreasing the brightness of the bright regions without losing image detail. At least one embodiment of the present invention automatically performs this function. In contrast, conventional methods of simply dividing the original image into a bright and shadow regions will not generally suffice to improve complex images. Images generally contain complex and diverse regions of varying contrast levels, and as a result, conventional methods generally produce inadequate results.
- In
step 210, original image A is provided. Original image A is an electronic representation of a subject and includes one or more characteristic values corresponding to specific locations, or pixels. Each pixel has one or more associated values, or planes, that represents information about a particular location on the subject. For original image A, the values corresponding to each pixel can be a measure of any suitable characteristic of the subject. For example, the values may represent the color, colors, luminance, incidence angle, x-ray density, or any other value representing a characteristic or combination of characteristics. - Original image A can be obtained in any suitable manner and need not correlate directly to conventional color images. One implementation obtains original image A by digitizing an image using a scanner, such as a flatbed, film scanner, and the like. Another implementation obtains original image A by directly capturing the image using a digital device, such as a digital camera, video camera, and the like. In yet another implementation, the original image A is captured using an imaging device, such as magnetic resonance imaging system, radar system, and the like. In this embodiment, the characteristic values do not correlate to colors but to other characteristics of the subject matter imaged. The original image A could also be obtained by computer generation or other similar technique. Dynamic image correction200 does not depend upon how the original image A is obtained, but only that the original image A includes one or more values that represent the image.
- In
step 220, a dynamic image mask B is generated from original image A. In the preferred embodiment, the pixel values of the dynamic image mask B are generated relative to a pixel in the original image A. In at least one embodiment, the pixels generated for dynamic image mask B are calculated using weighted averages of select pixels in original image A, as discussed in greater detail below. It will be appreciated that the pixels generated for dynamic image mask B may be calculated using any number of methods without departing from the spirit or the scope of the present invention. - Dynamic image mask B maintains the sharp edges in the original image A while blurring regions the surrounding the sharp edges. In effect, rapidly changing characteristics, i.e., values or contrast, in original image A are used to determine sharp edges in dynamic image mask B. At the same time, less rapidly changing values in original image A can be averaged to generate blurred regions in dynamic image mask B. In effect, the calculations performed on original image A produce a dynamic image mask B which preserves the boundaries between dissimilar pixels in original image A while blurring areas containing similar pixels, as will be discussed further in FIG. 3.
- A dynamic image mask B is often calculated for each characteristic value. For example, in the case of an original image having red, green, and blue values for each pixel, the red values are used to calculate the blurring and edge parameters of the dynamic image mask B for the red color, the blue values are used to calculate the blurring and edge parameters of the dynamic image mask B for the blue color, and so on. The dynamic image mask B can use different characteristics, or planes, to establish the regions and boundaries for different characteristics. For example, in the case of an original image having red, green, and blue color values for each pixel, the red values could be used to establish the blurring and edge parameters that are applied to each of the red, green, and blue values. Similarly, a calculated luminance value could be used to calculate the blurring and edge parameters that are then applied to the red, green, and blue values of each pixel. In other embodiments, a dynamic image mask B is only calculated for certain characteristics. Using the same example as above, a dynamic image mask B for the colors red and green may be calculated, but the values of the color blue are combined without change, as described in greater detail below.
- In
step 230, dynamic image mask B is applied to original image A to produce enhanced image C. Dynamic image mask B is generally applied to original image A by use of an overlay technique. As discussed further in FIG. 7, a mathematical operation, such as division between the pixel values of original image A and the corresponding pixel values in dynamic image mask B, can be used to generate the pixel values of enhanced image C. - In general, the process of generating and applying the dynamic image mask B is performed as part of a set of instructions run by an information processing system. The processes of
steps - In
step 240, the enhanced image C is delivered in the form desired. The form in which the enhanced image C is delivered includes, but is not limited to, a digital file, a photographic print, or a film record. Digital files can be stored on mass storage devices, tape drives, CD recorders, DVD recorders, and/or various forms of volatile or non-volatile memory. Digital files can also be transferred to other systems using a communications adapter, where the file can be sent to the Internet, an intranet, as an e-mail, etc. A digital file can also be prepared for retrieval at an image processing kiosk which allows customers to recover their pictures and print them out in a form of their choosing without the assistance of a film development technician. The enhanced image C can also be displayed as an image on a display or printed using a computer printer. The enhanced image C also can be represented on a form of film record, such as a film negative, positive image, or photographic print. In conventional printing processes, when an image is printed, a large portion of the dynamic range is lost. In contrast, enhanced image C generally contains desirable detail from original image A so that a larger quantity of image detail from original image A is effectively compressed into a dynamic range capable of being reproduced in print and can be preserved thereby. - Referring now to FIG. 3, a diagram of an original image and a blurred image are shown, according to one embodiment of the present invention. Original image A is composed of a plurality of pixels, such as pixels numbered301-325. Dynamic image mask B is composed of corresponding pixels, such as pixels numbered 351-375, calculated from the pixels of original image A. As described in greater detail below, the pixel values of dynamic image mask B are calculated using an averaging function that accounts for sharp edges.
- A sharp edge is generally defined by a variation between pixel values greater than a certain sharpness threshold, or Gain. In effect, the sharpness threshold allows the pixels to be differentiated into regions for purposes of averaging calculations. In some embodiments, the sharpness threshold is varied by a user. In other embodiments, the sharpness threshold is fixed within the software.
- The pixels calculated for dynamic image mask B correspond to averages taken over regions of pixels in original image A, taking into account the sharpness threshold, or Gain. For example,
pixel 363 corresponds to calculations performed aroundpixel 313. In one embodiment, provided that pixels 301-325 are similar, i.e., difference is below the sharpness threshold,pixel 363 is calculated by averaging the values of pixels 311-315, 303, 308, 318, and 323. In another embodiment,pixel 363 is calculated by averaging the values of pixels 307-309, 312-314, and 317-319. Any suitable number or selection process for the averaging process may be used without departing from the scope of the present invention. In the preferred embodiment, the pixels are assigned a weight based on their relative distance frompixel 313. In this embodiment, pixels that are relatively closer have a greater impact on the averaging calculation that pixels that are relatively remote. -
- The weight function, wN, can be used to apply a separate weight to each of the pixel values. Only values of wN between zero and one are accepted. Accordingly, if the value of wN is returned as a negative value, the returned weight for the pixel being weighed is zero. Using the first example above, if
pixel 313 was being calculated, wN could be used to apply a weight to each of the pixels 311-315, 303, 308, 318, and 323. PixelN is the contrast value of the pixel being weighed. Center pixel is the value of the central pixel, around which the blurring is being performed. Gain is a threshold value used to determine a contrast threshold for a sharp edge. For example, ifpixel 362 is being calculated and the difference in contrast betweenpixel 313 andpixel 308 is 15, with Gain set to 10, the returned value of wN is negative. Accordingly, since negative values are not allowed,pixel 308 is assigned a weight of zero, keeping the value ofpixel 308 from affecting the calculation ofpixel 362. - The value of Gain can be decreased as the pixel being weighed is further from the central pixel. Lowering the value of Gain allows small changes in the contrast between pixelN and centerpixel to result in negative wN, and thus be weighed to zero. Accordingly, in one embodiment, the farther the pixel is from the centerpixel, the smaller Gain gets and the more likely it is that the value of wN will be negative and the pixel will be assigned a weight of zero. The choice of Gain is chosen to preferably decrease slowly as the distance from the central pixel is increased. The values of Gain used can be adapted for the desired application; however, it has been found that slower changes in Gain provide images with more pleasing detail than sharper changes in Gain. Furthermore, the weight function itself can be altered without departing from the scope of the present invention.
- Once the weights of the surrounding pixels have been calculated, a sum of each of the pixel values, multiplied by their relative weights, can be calculated. The sum can then be divided by the sum of the individual weights to generate the weighted average of the pixels, which can be used for the pixels of dynamic image mask B. The minimum weight calculated from the pixels adjacent to the central pixel can also be used and multiplied by each of the pixels surrounding the central pixel. Multiplying by the weight of an adjacent pixel allows the blurring to be effectively turned “off” if the contrast around the central pixel is changing too rapidly. For example, if the difference in contrast between a central pixel and an adjacent pixel is large enough to warrant a sharp edge in dynamic image mask B, the weight of the adjacent pixel will be zero, forcing all other values to zero and allowing the central pixel to retain its value, effectively creating a sharp edge in dynamic image mask B.
- This embodiment of the processes performed to generate dynamic image mask B can be likened to a sandblaster. A sandblaster can be used to soften, or blur, the textures it is working over. Accordingly, the blurring algorithm as described above will be herein referred to as the sandblaster algorithm. A sandblaster has an effective radius over which it is used, with the material closer to the center of the sandblasting radius affected most. In the blurring algorithm described, a radius is selected and measured from the central pixel. The pressure of a sandblaster can be adjusted to affect more change. The Gain value in the described algorithm can be altered to affect more or less blurring. In at least one embodiment, the preferred radius is 4 and the preferred Gain is 40.
- The sandblaster algorithm can be performed in one dimensional increments. For example, to calculate the value of
pixel 362, thepixels surrounding pixel 312 are considered. In one embodiment of the present invention, the averaged pixel values are determined using the neighboring vertical pixels and then the neighboring horizontal pixel values, as described above. Alternatively, windows can be generated and applied to average in pixels around the central pixel together, in both the horizontal and vertical directions. Color images can compose multiple image planes, wherein the multiple image planes may include planes for each color, a red plane, a green plane, and a blue plane. In a preferred embodiment, the sandblaster algorithm is only performed on one plane at a time. Alternatively, the sandblaster algorithm can be calculated taking other image planes into account, calculating in the values of pixels relative to the central pixel from different color planes. However, it should be noted that performing multi-dimensional calculations over an image may increase the processing time. Additionally, pixels which are near an image edge, such aspixel 311 may ignore values desired from pixels beyond the limits of original image A. In one embodiment, the images along the edge use their value to reproduce pixel values beyond the image edge, for calculation with the sandblaster algorithm. Additionally, zeroes may be used for values lying outside the edges of original image A. - Referring now to FIG. 4, a graph of intensities across a row of intensities, before and after the sandblaster blurring algorithm has been applied is shown, according to at least one embodiment of the present invention.
Graph 450 represents the intensity values in an original image A around an edge representing contrasting intensity.Graph 460 represents the intensities for dynamic image mask B, among the same pixels asgraph 450. - Two distinct intensity levels are identifiable in
graph 450. A low intensity can be identified among pixels 451-454 and a high intensity region can be identified bypixel 465. The radius used to blur the pixels around the central pixel described in FIG. 3 is one factor in how much blurring will be performed. If too large a radius is used, little blurring may result. For example, if the pixel considered for blurring waspixel 451 and the radius was set large enough, the blurred value ofpixel 452 may not change much. With the radius set large enough,pixel 452 will be averaged with many pixels above its intensity, such aspixel 451.Pixel 452 will also be averaged with many pixels below its intensity, such aspixel 453. If the radius is too large, there could be enough pixels with intensities abovepixel 452 and enough pixels with intensities belowpixel 452 that the value forpixel 452 will remain unchanged since the intensity value ofpixel 452 lies between the high and low extremes. - Little blurring could also result from selecting too small a radius for blurring. In selecting a small radius, only the intensity values of pixels immediately by the selected pixel will be considered. For example, selecting
pixel 452 as the central pixel. If the radius is too small, allowing pixels only as far aspixel 451,pixels pixel 452. Selection of the radius has drastic effects to how much blurring is accomplished. The blurring radius must be large enough to average enough of a region of pixels while being small enough to effect enough blurring. In one embodiment, the blurring radius can be controlled automatically. As shown in FIG. 5, blurring can be performed over decimated representations of an original image using pyramidal decomposition. By performing a blurring algorithm and decimating the image, the effective radius of the blur is automatically increased as the image resolution is decreased. A decimated representation of the original image A can contain half the resolution of the original image A. Some of the detail in the original image is lost in the decimated representation. Performing blurring on the decimated image with a specific radius can relate to covering twice the radius in the original image. -
Graph 462 shows a graph of intensities in the dynamic image mask A using the sandblaster blurring algorithm. As can be seen, the blurring is enough to bring down the intensity ofpixel 452 in the original image topixel 462 in the blurred representation.Pixel 455, in a separate intensity level is increased in intensity topixel 465 in the blurred representation. In at least one embodiment, the blurring is turned off for pixels along an edge. Turning off the blurring allows the sharpness among edges to be preserved in the blurred representation, preserving edges between regions with a high contrast of intensities.Pixel 454 lies along an edge, where the intensity for pixels nearby, such aspixel 455, is much higher. The intensity ofpixel 454 is not changed, preserving the difference in contrast betweenpixel 454 and the pixels of higher intensity, such aspixel 455. - Referring now to FIG. 5, a block diagram of a method for generating another embodiment of a dynamic image mask B is illustrated. In this embodiment, the sandblaster algorithm can be used to create a blurred image with sharp edges and blurred regions. To improve the detail captured by an image mask incorporating the sandblaster blurring algorithm, a pyramidal decomposition is performed on the original image, as shown in FIG. 5. In
step 510, the original image A is received. - In
step 535, the image size is reduced. In at least one embodiment, the image size is reduced in half. The reduction in image size may be performed using a standard digital image decimation. In one embodiment, the decimation is performed by discarding every other pixel in the original image fromstep 510. - In
step 525, the sandblaster algorithm, as discussed in FIG. 3, is performed on the decimated image to create a blurred image. As previously discussed for FIG. 4, the decimated image contains half the resolution of the original image A. Some of the detail in the original image A is lost to the decimated image. By performing the sandblaster algorithm on the decimated image, the effective radius covered by the algorithm can relate to twice the radius in the original image. Since some of the detail from the original image A is not present in the decimated image, more blurring can result with the sandblaster algorithm. As the images described herein are decimated, the effective blur radius and amount of detail blurred increased in inverse proportion to the change in resolution in the decimated images. For example, performing the sandblast algorithm instep 525 to the reduced image ofstep 535 has twice the effective radius of performing the same algorithm to the original image, while the reduced image has half the resolution of the original image. - In
step 536, the blurred image is decimated once again. Instep 526, the decimated image fromstep 536 is blurred using the sandblaster algorithm. Further decimation steps 537-539 and sandblaster steps 527-329 are consecutively performed on the outputs of previous steps. Instep 550, the blurred image from thesandblaster step 529 is subtracted from the decimated output ofdecimation step 550. Instep 560, the mixed output fromstep 550 is up-sampled. In one embodiment, the image is increased to twice its pixel resolution. Increasing the image size may be performed by repeating the image values of present pixels to fill new pixels. Interpolation may also be performed to determine the values of the new pixels. in step 552, the up-sampled image fromstep 560, is added to the blurred image fromstep 528. The combined image information is subtracted from the decimated output fromstep 538. The calculations in step 552 are performed to recover image detail that may have been lost. Mixer steps 554 and 552, consecutively performed with up-sampling steps 562-366, attempt to generate mask data. Instep 558, a mixer is used to combine the up-sampled image data fromstep 566 with the blurred image data fromstep 525. The output from the mixer instep 558 is then up-sampled, instep 580, to produce the image mask of the received image. The dynamic image mask B is then prepared for delivery and use, as instep 590. - It will be appreciated that additional or less blurring may be performed among the steps of the pyramidal decomposition described herein. It should be noted that by not performing the blurring algorithm on the original image, a significant amount of processing time may be saved. Calculations based on the decimated images can be performed faster and with less overhead than calculations based off the original image, producing detailed image masks. The image masks produced using the described method preferably include sharp edges based on rapidly changing boundaries found in the original image A, and blurred regions among less rapidly changing boundaries. It should also be appreciated that more or less steps may be performed as part of the pyramidal decomposition described herein, without departing from the scope of the present invention.
- In the described embodiment, pyramidal decomposition is performed along a single image color plane. It will be appreciated that additional color planes may also be presented in the steps shown. Furthermore, multi-dimensional processing, wherein information from different color planes or planes of brightness is processed concurrently, may also be performed. According to at least one embodiment of the present invention, the resultant image mask generated is a monochrome mask, used to apply itself to the intensities of the individual image color planes in the original image. A monochrome image plane can be calculated from separate image color planes. For example, in one embodiment, the values of the monochrome image mask are determined using the following equation:
- OUT=MAX(R,G).
- OUT refers to the pixel being calculated in the monochromatic image mask. MAX(R,G) is a function in which the maximum intensity between the intensity value of the pixel in the red plane and the intensity value of the pixel in the green plane is chosen. In the case of a dynamic image mask pixel which contains more than 80% of its intensity from the blue plane, the formula can be appended to include:
- OUT=OUT+50% B.
- wherein 50% B is half of the intensity value in the blue plane. The dynamic image mask B may also be made to represent image intensities, such as the intensity among black and white values. It will be appreciated that while full color image masks may be used, they will require more processing overhead than using monochrome masks.
- Referring now to FIG. 6, a dynamic image mask B is illustrated, in comparison to a prior-art conventional cutout filter shown in FIG. 1, with properties representative of an dynamic image mask B created according to at least one embodiment of the present invention. The dynamic image mask B shown in FIG. 6 will be generally referred to as
revelation mask 650. The conventional image mask shown in FIG. 1 (prior-art) will be generally referred to asconventional filter 110. - The
revelation mask 650 maintains some of the detail lost to conventional image masks. Edges are preserved between regions of rapidly changing contrasts. For example, light region 690, generated to brighten detail within windows in the original image, maintains edges to show sharp contrast to thedarker region 680, which is generated to darken details in the walls shown in the original image. It should be noted that while edges are maintained between regions of rapidly changing contrasts, blurring is accomplished within the regions. For example, the details in the roof of the original image contain dark and light areas with a gradual shift in contrast. Inconventional filter 110,dark region 127 is generated to maintain contrast with the lighter areas in the tower on the roof. Whenconventional filter 110 is overlaid with the original image, the resultant image will show a sharp contrast difference betweendark region 127 andlight region 120 which does not maintain the gradual difference in the original image. In comparison,revelation mask 650 maintains the gradual shift in contrast as can be noted by the blurred shift in intensity between thetower region 655 and thelighter region 670, allowing the roof in the original image to maintain a gradual shift in intensity contrast while maintaining the sharp contrast ofdark region 655 against thedarker region 660, representing the background sky in the original image. - Referring now to FIG. 7, a method for generating an enhance image C in accordance with one embodiment of the present invention is illustrated. Image information related to an original image A is mathematically combined with information from dynamic image mask B. The combined data is used to create the enhanced image C.
- The enhanced image C is generated on a pixel by pixel basis. Each corresponding pixel from original image A and dynamic image mask B is combined to form a pixel in
masked image 710. For example, pixel data frompixel 715, of original image A, is combined with pixel information frompixel 735, of digital image mask B, using mathematical manipulation, such asoverlay function 720. The combined data is used to representpixel 715 of enhanced image C. -
- OUT refers to the value of the pixel in dynamic masked image B. IN refers to the value of the pixel taken from original image A. MASK refers to the value of the corresponding pixel in enhanced image C. For example, to produce the output value of
pixel 714, the value ofpixel 714 is divided by ¾ the value ofpixel 734, with the addition of an offset. The offset, ¼, is chosen to prevent an error from occurring due to diving by zero. The offset can also be chosen to lighten shadows in the resultantmasked image 710. - In one embodiment, the application of dynamic image mask B to original image A is performed through software run on a information processing system. As previously discussed, dynamic image mask B can be a monochromatic mask. The dynamic image mask B can be used to control the white and black levels in images. Grayscale contrast is the contrast over large areas in an image. Image contrast refers to the contrast of details within an image. Through manipulation of the proportion of the value of MASK and the offset used in
overlay function 720, the grayscale contrast and the image contrast can be altered to best enhance theenhanced image C 310. In one embodiment of the present invention,overlay function 720 is altered according to settings made by a user. Independent control of the image contrast and grayscale contrast can be provided. Control can be used to produce images using low image contrast in highlights and high image contrast in shadows. Additionally, functions can be added to control the generation of the dynamic image mask B. Control can be offered over the pressure (Gain) and radius (region) effected through the sandblaster algorithm (described in FIG. 3). Additionally, control over the histogram of the image can be offered through control over the image contrast and the grayscale contrast. A normalized image can be generated in which histogram leveling can be performed without destroying image contrast. The controls, functions, and algorithms described herein can be performed within an information processing system. It will be appreciated that other systems may be employed, such as through image processing kiosks, to produce enhanced image C, in keeping with the scope of the present invention. - Referring to FIG. 8A, a
wrinkle reduction process 800 in accordance with one embodiment of the present invention is illustrated. As described in greater detail below, this embodiment of thewrinkle reduction process 800 operates to suppress median frequencies without suppressing high definition detail or low frequency contrast. As a result, people have a younger look without sacrificing detail. - In the embodiment illustrated, a dynamic image mask B is calculated from original image A, as shown by
block 802. In the preferred embodiment, the dynamic image mask B is calculated using a radius of 5 and a Gain of 64, as discussed in FIG. 3. The dynamic image mask B is then passed through alow pass filter 804. Thelow pass filter 804 is preferably a “soft focus” filter. In one embodiment, thelow pass filter 804 is calculated as the average of a Gaussian average with a radius of one and a Gaussian average with a radius of three. Other types of low pass filters may be used without departing from the scope of the present invention. - The original image A is also passed through a
high pass filter 806. In one embodiment, thehigh pass filter 806 is calculated as the inverse of the average of the Gaussian average with a blur of one and a gaussian average with a blur of three. Other types of high pass filters may be used without departing from the scope of the present invention. - The results from the
low pass filter 804 and thehigh pass filter 806 are then added together to form amedian mask 808. Themedian mask 808 can then be applied to the original image A using, for example,applicator 810 to produce an enhanced image. In the preferred embodiment, theapplicator 810 is an electronic brush that can be varied by radius to apply themedian mask 808 only to those areas of the original image A specified by the user. Other types ofapplicators 810 may be used to apply themedian mask 808 to the original image A. - FIG. 8B-1 illustrates an untouched original image 820, and FIG. 8B-2 illustrates the same image after having the
wrinkle reduction process 800 applied to the image 820. As can be seen, thewrinkle reduction process 800 reduces the viable affects of age of the person in the image, without sacrificing the minute detail of the image and without apparent blurring or softening of the details. This creates a more pleasing image to the eye and most importantly, more pleasing to the person in the picture. The same process can be applied to other parts of the image to produce similar results. For example, when applied to clothing, thewrinkle reduction process 800 produces the appearance of a freshly pressed shirt or pants without affecting the details or appearing blurry. Although only a few of the applications of thewrinkle reduction process 800 and dynamic image mask B have been illustrated, it should be understood that they may be used for any suitable purpose or combination without departing from the scope of the present invention. - Referring to FIG. 9, an
image capture system 900 used to implement one or more embodiments of the present invention is illustrated.Image capture system 900 includes any device capable of capturing data representative of an image and subsequently processing the data according to the teachings set forth herein. For example,image capture system 900 could include a digital camera, video recorder, a scanner, image processing software, and the like. An embodiment whereimage capture system 900 includes a digital camera is discussed subsequently for ease of illustration. The following discussion may be applied to other embodiments ofimage capture system 900 without departing from the spirit or scope of the present invention. -
Image capture system 900 includes, but is not limited to,image sensor 910, analog-to-digital (A/D)convertor 920,color decoder 930,color management system 940,storage system 950, and/ordisplay 960. In at least one embodiment,image capture system 900 is connected toprinter 980 via a serial cable, printer cable, universal serial bus, networked connection, and the like.Image sensor 910, in one embodiment, captures an image and converts the captured image into electrical information representative of the image.Image sensor 910 could include an image sensor on a digital camera, such as a charge coupled device (CCD) sensor, complementary metal oxide semiconductor sensor, and the like. For example, a CCD sensor converts photons reflected off of or transmitted through a subject into stored electrical charge at the location of each photosite of the CCD sensor. The stored electrical charge of each photosite is then used to obtain a value associated with the photosite. Each photosite could have a one-to-one correspondence with the pixels of the resulting image, or photosites are used in conjunction to determine the value of one or more pixels. - In one embodiment,
image sensor 910 sends electrical information representing a captured image to A/D convertor 920 in analog form, which converts the electrical information from an analog form to a digital form. Alternatively, in one embodiment,image sensor 910 captures an image and outputs the electrical information representing the image in digital form. It will be appreciated that, in this case, A/D convertor 920 would not be necessary. - It will be appreciated that photosites on image sensors, such as CCDs, often only measure the magnitude or intensity of the light striking a photosite. In this case, a number of methods may be used to convert the intensity values of the photosites (i.e. a black and white image) into corresponding color values for each photosite. For example, one method of obtaining color information is to use a beam splitter to focus the image onto more than one image sensor. In this case, each image sensor has a filter associated with a color. For example,
image sensor 910 could include three CCD sensors, where one CCD sensor is filtered for red light, another CCD sensor is filtered for green light, and the third sensor is filtered for blue light. Another method is to use a rotating device having separate color filters between the light source (the image) andimage sensor 910. As each color filter rotates in front ofimage sensor 910, a separate image corresponding to the color filter is captured. For example, a rotating disk could have a filter for each of the primary colors red, blue and green. In this case, the disk would rotate a red filter, a blue filter, and a green filter sequentially in front ofimage sensor 910, and as each filter was rotated in front, a separate image would be captured. - Alternatively, a permanent filter could be placed over each individual photosite. By breaking up
image sensor 910 into a variety of different photosites associated with different colors, the actual color associated with a specific point or pixel of a captured element may be interpolated. For example, a common pattern used is the Bayer filter pattern, where rows of red and green sensitive photosites are alternated with rows of blue and green photosites. In the Bayer filter pattern, there is often many more green color sensitive photosites than there are blue or red color sensitive photosites, as the human eye is more sensitive to green than the others, so more green color information should be present for a captured image to be perceived as “true color” by the human eye. - Accordingly, in one embodiment,
color decoder 930 receives the digital output representing an image from A/D convertor 920 and converts the information from intensity values (black-and-white) to color values. For example,image sensor 910 could utilize a Bayer filter pattern as discussed previously. In this case, the black-and-white digital output from A/D convertor 920 could be interpolated or processed to generate data representative of one or more color images. For example,color decoder 930 could generate data representative of one or more full color images, one or more monochrome images, and the like. - Using the data representative of an image generated by
color decoder 930, in one embodiment,color management system 940 processes the data for output and/or storage. For example,color management system 940 could attenuate the dynamic range of the data fromcolor decoder 930. This may be done to reduce the amount of data associated with a captured image.Color management 940 could also format the data into a variety of formats, such as a Joint Picture Experts Group (JPEG) format, a tagged image file format (TIFF), a bitmap format, and the like.Color management system 940 may perform a number of other processes or methods to prepare the data representative of an image for display or output, such as compressing the data, converting the data from an analog to a digital format, etc. - After
color management system 940 processes data representative of an image, the data, in one embodiment, is stored onstorage 950 and/or displayed ondisplay 960.Storage 950 could include memory, such as removable flash memory for a digital camera, a storage disk, such as a hard drive or a floppy disk, and the like.Display 960 could include a liquid crystal display (LCD), a cathode ray tube (CRT) display, and other devices used to display or preview captured images. In an alternative embodiment, the data representative of an image could be processed byprinter driver 970 to be printed byprinter 970.Printer 970 could include a photograph printer, a desktop printer, a copier machine, a fax machine, a laser printer, and the like.Printer driver 970 could be collocated, physically or logically, withprinter 970, on a computer connected toprinter 960, and the like. It will be appreciated that one or more of the elements ofimage capture system 900 may be implemented as a state machine, as combinational logic, as software executable on a data processor, and the like. It will also be appreciated that the method or processes performed by one or more of the elements ofimage capture system 900 may be performed by a single device or system. For example,color decoder 930 andcolor management 940 could be implemented as a monolithic microprocessor or as a combined set of executable instructions. -
Image capture system 900 can be used to implement one or more methods of various embodiments of the present invention. The methods, herein referred to collectively as the image mask method, may be implemented at one or more stages of the image capturing process ofimage system 900. In one embodiment, the image mask method may be applied atstage 925 between the output of digital data from A/D convertor 920 and the input ofcolor decoder 930. In many cases,stage 925 may be the optimal location for application of the image mask method. For example, if data representative of an image output fromimage sensor 910 is monochrome (or black-and-white) information yet to be decoded into color information, less information may need to be processed using the image mask method than after conversion of the data to color information. For example, if the data were to be decoded into the three primary colors (red, blue, green), three times of information may need to be processed, as there are three colors associated with each pixel of a captured image. The image mask method, according to at least one embodiment discussed previously, does not affect the accuracy or operation ofcolor decoder 930. - Alternatively, the image mask method may be applied at
stage 935 betweencolor decoder 930 andcolor management system 940. In some situations, the location ofstage 935 may not be as optimal asstage 925, since there may be more data to process betweencolor decoder 930 andcolor management system 940. For example, color decoder 93 0 could generate data for each of the primary colors, resulting in three times the information to be processed by the image mask method atstage 935. An image mask method may also be implemented atstage 945 betweencolor management system 940 andstorage 950 and/ordisplay 960. However, since the data output bycolor management system 940 often has been processed which may result in compression and/or loss of information and dynamic range, therefore application of the image mask method atstage 945 may not generate results as favorable as atstages - If the captured image is to be printed, the image mask method may be implemented at
stages stage 965, the image mask method may be implemented byprinter driver 970, while atstage 965, the image mask method may be implemented betweenprinter driver 970 andprinter 980. For example, the connection between a system connected toprinter driver 970, such as a computer, andprinter 980 could include software and/or hardware to implement the image mask method. However, as discussed with reference to stage 945, the data representative of a captured image to be printed may have reduced dynamic range and/or loss of other information as a result of processing bycolor management system 940. - In at least one embodiment, the image mask method performed at
stages processor 942.Processor 942 can include a microprocessor, a state machine, combinational logic circuitry, and the like. In one implementation, the set of instructions are stored and retrieved frommemory 943, wherememory 943 can include random access memory, read only memory, flash memory, a storage device, and the like. Note thatprocessor 942, in one embodiment, also executes instructions for performing the operations of one or more of the elements ofimage capture system 900. For example,processor 942 could execute instructions to perform the color decoding operations performed bycolor decoder 930 and then execute the set of instructions representative of the image mask method atstage 935. - It will be appreciated that the cost or effort to implement the image mask method at an optimal or desired stage (stages925-975) may be prohibitive, resulting in the implementation of the image mask method at an alternate stage. For example, although
stage 925 is often the optimal location for implementation of the image mask method, for reasons discussed previously, it may be difficult to implement the image mask method at this location. For example,image sensor 910, A/D convertor 920, andcolor decoder 930 could be implemented as a monolithic electronic circuit. In this case it might prove difficult to modify the circuit to implement the method. Alternatively, more than one element ofimage capture system 900, such ascolor decoder 930 andcolor management system 940, may be implemented as a single software application. In this case, the software application may be proprietary software where modification is prohibited, or the source code of the software may not be available, making modification of the software application difficult. - In the event that the image mask method may not be implemented in the optimal location, application of the image mask method in a more suitable location often will result in improved image quality and detail. For example, even though the dynamic range of data representative of an image may be reduced after processing by
color management system 940, application of the image mask method atstage 945, in one embodiment, results in data representative of an image having improved quality and/or detail over the data output bycolor management system 940. The improved image data may result in an improved image for display ondisplay 960, subsequent display when retrieved fromstorage 960, or physical replication byprinter 980. It will be appreciated that the image mask method may be employed more than once. For example, the image mask method may be employed atstage 925 to perform an initial compression of the dynamic range of the image, and then again atstage 945 for to further compress the images dynamic range. - Referring now to FIG. 10, a chart showing various improvements in image types is illustrated according to at least one embodiment of the present invention. As discussed previously, an implementation of at least one embodiment of the present invention may be used to improve the dynamic range of representations of captured images. The horizontal axis of
chart 1000 represents the dynamic range of various types of image representations. The dynamic range of images, as presented to the human eye, (i.e. “real life”) is represented byrange 1006. The dynamic range decreases sequentially from real life (range 1006) to printed transparencies (range 1005), CRT displays (range 1004), glossy photographic prints (range 1003), matte photographic prints (range 1002), and LCD displays (range 1001). Note that the sequence of dynamic ranges of various image representations is a general comparison and the sequence of dynamic ranges should not be taken as absolute in all cases. For example, there could exist a CRT display (range 1004) which could have a dynamic range greater than printed transparencies (range 1005). - According to at least one embodiment, by applying an image mask method disclosed herein, the dynamic range of the representation of an image may be improved. For example, by applying an image mask method sometime before data representing an image is displayed on an LCD monitor, image information having a dynamic range comparable to a glossy photographic print (range1003), could be compressed for display on an LCD monitor having a
dynamic range 1001, resulting in an improved display image. Likewise, image information having a dynamic range equivalent to a CRT display (range 1004) may be compressed into a dynamic range usable for matte photographic prints (range 1002), and so on. As a result, an image mask method, as disclosed herein, may be used to improve the dynamic range used for display of a captured image, thereby improving the quality of the display of the captured image. - One of the preferred implementations of the invention is as sets of computer readable instructions resident in the random access memory of one or more processing systems configured generally as described in FIGS.1-10. Until required by the processing system, the set of instructions may be stored in another computer readable memory, for example, in a hard disk drive or in a removable memory such as an optical disk for eventual use in a CD drive or DVD drive or a floppy disk for eventual use in a floppy disk drive. Further, the set of instructions can be stored in the memory of another image processing system and transmitted over a local area network or a wide area network, such as the Internet, where the transmitted signal could be a signal propagated through a medium such as an ISDN line, or the signal may be propagated through an air medium and received by a local satellite to be transferred to the processing system. Such a signal may be a composite signal comprising a carrier signal, and contained within the carrier signal is the desired information containing at least one computer program instruction implementing the invention, and may be downloaded as such when desired by the user. One skilled in the art would appreciate that the physical storage and/or transfer of the sets of instructions physically changes the medium upon which it is stored electrically, magnetically, or chemically so that the medium carries computer readable information. The preceding detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims.
- In the preceding detailed description of the figures, reference has been made to the accompanying drawings which form a part thereof, and in which is shown by way of illustration specific preferred embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that logical, mechanical, chemical and electrical changes may be made without departing from the spirit or scope of the invention. To avoid detail not necessary to enable those skilled in the art to practice the invention, the description may omit certain information known to those skilled in the art. Furthermore, many other varied embodiments that incorporate the teachings of the invention may be easily constructed by those skilled in the art. Accordingly, the present invention is not intended to be limited to the specific form set forth herein, but on the contrary, it is intended to cover such alternatives, modifications, and equivalents, as can be reasonably included within the spirit and scope of the invention. The preceding detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims.
Claims (48)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/960,276 US20020176113A1 (en) | 2000-09-21 | 2001-09-21 | Dynamic image correction and imaging systems |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US23440800P | 2000-09-21 | 2000-09-21 | |
US23452000P | 2000-09-21 | 2000-09-21 | |
US28559101P | 2001-04-19 | 2001-04-19 | |
US09/960,276 US20020176113A1 (en) | 2000-09-21 | 2001-09-21 | Dynamic image correction and imaging systems |
Publications (1)
Publication Number | Publication Date |
---|---|
US20020176113A1 true US20020176113A1 (en) | 2002-11-28 |
Family
ID=27398563
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/960,239 Expired - Fee Related US7016080B2 (en) | 2000-09-21 | 2001-09-21 | Method and system for improving scanned image detail |
US09/960,276 Abandoned US20020176113A1 (en) | 2000-09-21 | 2001-09-21 | Dynamic image correction and imaging systems |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/960,239 Expired - Fee Related US7016080B2 (en) | 2000-09-21 | 2001-09-21 | Method and system for improving scanned image detail |
Country Status (7)
Country | Link |
---|---|
US (2) | US7016080B2 (en) |
EP (1) | EP1323292A2 (en) |
JP (1) | JP2004517384A (en) |
CN (1) | CN1474997A (en) |
AU (1) | AU2001294669A1 (en) |
TW (1) | TW538382B (en) |
WO (1) | WO2002025928A2 (en) |
Cited By (31)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020103006A1 (en) * | 2001-01-31 | 2002-08-01 | Steven Doe | Liquid crystal display device |
US20040051794A1 (en) * | 2002-09-12 | 2004-03-18 | Pentax Corporation | Filter process |
US20040066458A1 (en) * | 2002-07-12 | 2004-04-08 | Hiroyuki Kawamura | Imaging system |
EP1443459A2 (en) * | 2003-02-03 | 2004-08-04 | Noritsu Koki Co., Ltd. | Image processing method and apparatus for correcting photographic images |
US20050057484A1 (en) * | 2003-09-15 | 2005-03-17 | Diefenbaugh Paul S. | Automatic image luminance control with backlight adjustment |
US20050286794A1 (en) * | 2004-06-24 | 2005-12-29 | Apple Computer, Inc. | Gaussian blur approximation suitable for GPU |
US7016080B2 (en) * | 2000-09-21 | 2006-03-21 | Eastman Kodak Company | Method and system for improving scanned image detail |
US20060182363A1 (en) * | 2004-12-21 | 2006-08-17 | Vladimir Jellus | Method for correcting inhomogeneities in an image, and an imaging apparatus therefor |
US20060232823A1 (en) * | 2005-04-13 | 2006-10-19 | Hooper David S | Image contrast enhancement |
US20060285164A1 (en) * | 2005-06-21 | 2006-12-21 | Chun-Yi Wang | Method for Processing Multi-layered Image Data |
US20070244844A1 (en) * | 2006-03-23 | 2007-10-18 | Intelliscience Corporation | Methods and systems for data analysis and feature recognition |
US20080033984A1 (en) * | 2006-04-10 | 2008-02-07 | Intelliscience Corporation | Systems and methods for data point processing |
US20080031548A1 (en) * | 2006-03-23 | 2008-02-07 | Intelliscience Corporation | Systems and methods for data transformation |
WO2008022222A2 (en) * | 2006-08-15 | 2008-02-21 | Intelliscience Corporation | Systems and methods for data transformation |
US20080170801A1 (en) * | 2005-09-05 | 2008-07-17 | Algosoft Limited | Automatic digital film and video restoration |
CN101593267A (en) * | 2008-05-27 | 2009-12-02 | 三星电子株式会社 | The method of display label, display tag system and writing display tag information |
US20100202262A1 (en) * | 2009-02-10 | 2010-08-12 | Anchor Bay Technologies, Inc. | Block noise detection and filtering |
US20100309344A1 (en) * | 2009-06-05 | 2010-12-09 | Apple Inc. | Chroma noise reduction for cameras |
US20100309345A1 (en) * | 2009-06-05 | 2010-12-09 | Apple Inc. | Radially-Based Chroma Noise Reduction for Cameras |
US20110242367A1 (en) * | 2010-03-31 | 2011-10-06 | Samsung Electronics Co., Ltd. | Image processing method and photographing apparatus using the same |
US8175992B2 (en) | 2008-03-17 | 2012-05-08 | Intelliscience Corporation | Methods and systems for compound feature creation, processing, and identification in conjunction with a data analysis and feature recognition system wherein hit weights are summed |
WO2013078182A1 (en) * | 2011-11-21 | 2013-05-30 | Georgetown University | System and method for enhancing the legibility of degraded images |
KR20130069494A (en) * | 2011-12-16 | 2013-06-26 | 지멘스 악티엔게젤샤프트 | Method to create an mr image of an examination subject with a magnetic resonance system, as well as a corresponding magnetic resonance system |
US20130230244A1 (en) * | 2012-03-02 | 2013-09-05 | Chintan Intwala | Continuously Adjustable Bleed for Selected Region Blurring |
US8559746B2 (en) | 2008-09-04 | 2013-10-15 | Silicon Image, Inc. | System, method, and apparatus for smoothing of edges in images to remove irregularities |
US8625885B2 (en) | 2006-03-23 | 2014-01-07 | Intelliscience Corporation | Methods and systems for data analysis and feature recognition |
CN103679656A (en) * | 2013-10-21 | 2014-03-26 | 厦门美图网科技有限公司 | Automatic image sharpening method |
US20160078601A1 (en) * | 2014-09-12 | 2016-03-17 | Tmm, Inc. | Image upsampling using local adaptive weighting |
US10510153B1 (en) * | 2017-06-26 | 2019-12-17 | Amazon Technologies, Inc. | Camera-level image processing |
US10580149B1 (en) * | 2017-06-26 | 2020-03-03 | Amazon Technologies, Inc. | Camera-level image processing |
US20230200639A1 (en) * | 2021-12-27 | 2023-06-29 | Novasight Ltd. | Method and device for treating / preventing refractive errors as well as for image processing and display |
Families Citing this family (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6927804B2 (en) * | 2002-09-09 | 2005-08-09 | Eastman Kodak Company | Reducing color aliasing artifacts from color digital images |
US20040116796A1 (en) * | 2002-12-17 | 2004-06-17 | Jianying Li | Methods and apparatus for scoring a substance |
EP1605402A2 (en) * | 2004-06-10 | 2005-12-14 | Sony Corporation | Image processing device and method, recording medium, and program for blur correction |
US7782339B1 (en) | 2004-06-30 | 2010-08-24 | Teradici Corporation | Method and apparatus for generating masks for a multi-layer image decomposition |
US8442311B1 (en) | 2005-06-30 | 2013-05-14 | Teradici Corporation | Apparatus and method for encoding an image generated in part by graphical commands |
JP2006098803A (en) * | 2004-09-29 | 2006-04-13 | Toshiba Corp | Moving image processing method, moving image processing apparatus and moving image processing program |
JP4893079B2 (en) * | 2006-04-14 | 2012-03-07 | ソニー株式会社 | Boundary value table optimization device, liquid discharge head, liquid discharge device, and computer program |
JP2008067230A (en) * | 2006-09-08 | 2008-03-21 | Sony Corp | Image processing apparatus, image processing method, and program |
TWI408486B (en) * | 2008-12-30 | 2013-09-11 | Ind Tech Res Inst | Camera with dynamic calibration and method thereof |
EP2333623A1 (en) * | 2009-12-11 | 2011-06-15 | Siemens Aktiengesellschaft | Monitoring system for data acquisition in a production environment |
US20110210960A1 (en) * | 2010-02-26 | 2011-09-01 | Google Inc. | Hierarchical blurring of texture maps |
US8285069B2 (en) | 2010-03-30 | 2012-10-09 | Chunghwa Picture Tubes, Ltd. | Image processing device and method thereof |
CN101882306B (en) * | 2010-06-13 | 2011-12-21 | 浙江大学 | High-precision joining method of uneven surface object picture |
TWI492096B (en) | 2010-10-29 | 2015-07-11 | Au Optronics Corp | 3d image interactive system and position-bias compensation method of the same |
US9031346B2 (en) * | 2011-01-07 | 2015-05-12 | Tp Vision Holding B.V. | Method for converting input image data into output image data, image conversion unit for converting input image data into output image data, image processing apparatus, display device |
US9646366B2 (en) * | 2012-11-30 | 2017-05-09 | Change Healthcare Llc | Method and apparatus for enhancing medical images |
WO2018195188A1 (en) * | 2017-04-19 | 2018-10-25 | Schneider Electric It Corporation | Systems and methods of proximity detection for rack enclosures |
CN110830727B (en) * | 2018-08-07 | 2021-06-22 | 浙江宇视科技有限公司 | Automatic exposure ratio adjusting method and device |
CN112394536B (en) * | 2019-07-31 | 2022-04-29 | 华为技术有限公司 | Optical anti-shake device and control method |
TWI768709B (en) * | 2021-01-19 | 2022-06-21 | 福邦科技國際股份有限公司 | Dual image fusion method and device |
Citations (98)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US2404138A (en) * | 1941-10-06 | 1946-07-16 | Alvin L Mayer | Apparatus for developing exposed photographic prints |
US3250689A (en) * | 1965-05-03 | 1966-05-10 | Robert G Seyl | Simplified method of measuring corrosion using reference electrode |
US3520690A (en) * | 1965-06-25 | 1970-07-14 | Fuji Photo Film Co Ltd | Process for controlling dye gradation in color photographic element |
US3587435A (en) * | 1969-04-24 | 1971-06-28 | Pat P Chioffe | Film processing machine |
US3615479A (en) * | 1968-05-27 | 1971-10-26 | Itek Corp | Automatic film processing method and apparatus therefor |
US3615498A (en) * | 1967-07-29 | 1971-10-26 | Fuji Photo Film Co Ltd | Color developers containing substituted nbenzyl-p-aminophenol competing developing agents |
US3747120A (en) * | 1971-01-11 | 1973-07-17 | N Stemme | Arrangement of writing mechanisms for writing on paper with a coloredliquid |
US3833161A (en) * | 1972-02-08 | 1974-09-03 | Bosch Photokino Gmbh | Apparatus for intercepting and threading the leader of convoluted motion picture film or the like |
US3903541A (en) * | 1971-07-27 | 1975-09-02 | Meister Frederick W Von | Apparatus for processing printing plates precoated on one side only |
US3946398A (en) * | 1970-06-29 | 1976-03-23 | Silonics, Inc. | Method and apparatus for recording with writing fluids and drop projection means therefor |
US3959048A (en) * | 1974-11-29 | 1976-05-25 | Stanfield James S | Apparatus and method for repairing elongated flexible strips having damaged sprocket feed holes along the edge thereof |
US4026756A (en) * | 1976-03-19 | 1977-05-31 | Stanfield James S | Apparatus for repairing elongated flexible strips having damaged sprocket feed holes along the edge thereof |
US4081577A (en) * | 1973-12-26 | 1978-03-28 | American Hoechst Corporation | Pulsed spray of fluids |
US4142107A (en) * | 1977-06-30 | 1979-02-27 | International Business Machines Corporation | Resist development control system |
US4215927A (en) * | 1979-04-13 | 1980-08-05 | Scott Paper Company | Lithographic plate processing apparatus |
US4249985A (en) * | 1979-03-05 | 1981-02-10 | Stanfield James S | Pressure roller for apparatus useful in repairing sprocket holes on strip material |
US4265545A (en) * | 1979-07-27 | 1981-05-05 | Intec Corporation | Multiple source laser scanning inspection system |
US4501480A (en) * | 1981-10-16 | 1985-02-26 | Pioneer Electronic Corporation | System for developing a photo-resist material used as a recording medium |
US4564280A (en) * | 1982-10-28 | 1986-01-14 | Fujitsu Limited | Method and apparatus for developing resist film including a movable nozzle arm |
US4594598A (en) * | 1982-10-26 | 1986-06-10 | Sharp Kabushiki Kaisha | Printer head mounting assembly in an ink jet system printer |
US4636808A (en) * | 1985-09-09 | 1987-01-13 | Eastman Kodak Company | Continuous ink jet printer |
US4666307A (en) * | 1984-01-19 | 1987-05-19 | Fuji Photo Film Co., Ltd. | Method for calibrating photographic image information |
US4670779A (en) * | 1984-01-10 | 1987-06-02 | Sharp Kabushiki Kaisha | Color-picture analyzing apparatus with red-purpose and green-purpose filters |
US4736221A (en) * | 1985-10-18 | 1988-04-05 | Fuji Photo Film Co., Ltd. | Method and device for processing photographic film using atomized liquid processing agents |
US4741621A (en) * | 1986-08-18 | 1988-05-03 | Westinghouse Electric Corp. | Geometric surface inspection system with dual overlap light stripe generator |
US4745040A (en) * | 1976-08-27 | 1988-05-17 | Levine Alfred B | Method for destructive electronic development of photo film |
US4755844A (en) * | 1985-04-30 | 1988-07-05 | Kabushiki Kaisha Toshiba | Automatic developing device |
US4777102A (en) * | 1976-08-27 | 1988-10-11 | Levine Alfred B | Method and apparatus for electronic development of color photographic film |
US4796061A (en) * | 1985-11-16 | 1989-01-03 | Dainippon Screen Mfg. Co., Ltd. | Device for detachably attaching a film onto a drum in a drum type picture scanning recording apparatus |
US4814630A (en) * | 1987-06-29 | 1989-03-21 | Ncr Corporation | Document illuminating apparatus using light sources A, B, and C in periodic arrays |
US4821114A (en) * | 1986-05-02 | 1989-04-11 | Dr. Ing. Rudolf Hell Gmbh | Opto-electronic scanning arrangement |
US4845551A (en) * | 1985-05-31 | 1989-07-04 | Fuji Photo Film Co., Ltd. | Method for correcting color photographic image data on the basis of calibration data read from a reference film |
US4851311A (en) * | 1987-12-17 | 1989-07-25 | Texas Instruments Incorporated | Process for determining photoresist develop time by optical transmission |
US4857430A (en) * | 1987-12-17 | 1989-08-15 | Texas Instruments Incorporated | Process and system for determining photoresist development endpoint by effluent analysis |
US4875067A (en) * | 1987-07-23 | 1989-10-17 | Fuji Photo Film Co., Ltd. | Processing apparatus |
US4994918A (en) * | 1989-04-28 | 1991-02-19 | Bts Broadcast Television Systems Gmbh | Method and circuit for the automatic correction of errors in image steadiness during film scanning |
US5027146A (en) * | 1989-08-31 | 1991-06-25 | Eastman Kodak Company | Processing apparatus |
US5034767A (en) * | 1987-08-28 | 1991-07-23 | Hanetz International Inc. | Development system |
US5101286A (en) * | 1990-03-21 | 1992-03-31 | Eastman Kodak Company | Scanning film during the film process for output to a video monitor |
US5124216A (en) * | 1990-07-31 | 1992-06-23 | At&T Bell Laboratories | Method for monitoring photoresist latent images |
US5155596A (en) * | 1990-12-03 | 1992-10-13 | Eastman Kodak Company | Film scanner illumination system having an automatic light control |
US5196285A (en) * | 1990-05-18 | 1993-03-23 | Xinix, Inc. | Method for control of photoresist develop processes |
US5200817A (en) * | 1991-08-29 | 1993-04-06 | Xerox Corporation | Conversion of an RGB color scanner into a colorimetric scanner |
US5212512A (en) * | 1990-11-30 | 1993-05-18 | Fuji Photo Film Co., Ltd. | Photofinishing system |
US5231439A (en) * | 1990-08-03 | 1993-07-27 | Fuji Photo Film Co., Ltd. | Photographic film handling method |
US5235352A (en) * | 1991-08-16 | 1993-08-10 | Compaq Computer Corporation | High density ink jet printhead |
US5255408A (en) * | 1992-02-11 | 1993-10-26 | Eastman Kodak Company | Photographic film cleaner |
US5296923A (en) * | 1991-01-09 | 1994-03-22 | Konica Corporation | Color image reproducing device and method |
US5334247A (en) * | 1991-07-25 | 1994-08-02 | Eastman Kodak Company | Coater design for low flowrate coating applications |
US5350651A (en) * | 1993-02-12 | 1994-09-27 | Eastman Kodak Company | Methods for the retrieval and differentiation of blue, green and red exposure records of the same hue from photographic elements containing absorbing interlayers |
US5350664A (en) * | 1993-02-12 | 1994-09-27 | Eastman Kodak Company | Photographic elements for producing blue, green, and red exposure records of the same hue and methods for the retrieval and differentiation of the exposure records |
US5357307A (en) * | 1992-11-25 | 1994-10-18 | Eastman Kodak Company | Apparatus for processing photosensitive material |
US5391443A (en) * | 1991-07-19 | 1995-02-21 | Eastman Kodak Company | Process for the extraction of spectral image records from dye image forming photographic elements |
US5414779A (en) * | 1993-06-14 | 1995-05-09 | Eastman Kodak Company | Image frame detection |
US5416550A (en) * | 1990-09-14 | 1995-05-16 | Eastman Kodak Company | Photographic processing apparatus |
US5418119A (en) * | 1993-07-16 | 1995-05-23 | Eastman Kodak Company | Photographic elements for producing blue, green and red exposure records of the same hue |
US5418597A (en) * | 1992-09-14 | 1995-05-23 | Eastman Kodak Company | Clamping arrangement for film scanning apparatus |
US5436738A (en) * | 1992-01-22 | 1995-07-25 | Eastman Kodak Company | Three dimensional thermal internegative photographic printing apparatus and method |
US5440365A (en) * | 1993-10-14 | 1995-08-08 | Eastman Kodak Company | Photosensitive material processor |
US5448380A (en) * | 1993-07-31 | 1995-09-05 | Samsung Electronics Co., Ltd. | color image processing method and apparatus for correcting a color signal from an input image device |
US5447811A (en) * | 1992-09-24 | 1995-09-05 | Eastman Kodak Company | Color image reproduction of scenes with preferential tone mapping |
US5452018A (en) * | 1991-04-19 | 1995-09-19 | Sony Electronics Inc. | Digital color correction system having gross and fine adjustment modes |
US5496669A (en) * | 1992-07-01 | 1996-03-05 | Interuniversitair Micro-Elektronica Centrum Vzw | System for detecting a latent image using an alignment apparatus |
US5516608A (en) * | 1994-02-28 | 1996-05-14 | International Business Machines Corporation | Method for controlling a line dimension arising in photolithographic processes |
US5519510A (en) * | 1992-07-17 | 1996-05-21 | International Business Machines Corporation | Electronic film development |
US5546477A (en) * | 1993-03-30 | 1996-08-13 | Klics, Inc. | Data compression and decompression |
US5550566A (en) * | 1993-07-15 | 1996-08-27 | Media Vision, Inc. | Video capture expansion card |
US5552904A (en) * | 1994-01-31 | 1996-09-03 | Samsung Electronics Co., Ltd. | Color correction method and apparatus using adaptive region separation |
US5563717A (en) * | 1995-02-03 | 1996-10-08 | Eastman Kodak Company | Method and means for calibration of photographic media using pre-exposed miniature images |
US5568270A (en) * | 1992-12-09 | 1996-10-22 | Fuji Photo Film Co., Ltd. | Image reading apparatus which varies reading time according to image density |
US5596415A (en) * | 1993-06-14 | 1997-01-21 | Eastman Kodak Company | Iterative predictor-based detection of image frame locations |
US5627016A (en) * | 1996-02-29 | 1997-05-06 | Eastman Kodak Company | Method and apparatus for photofinishing photosensitive film |
US5649260A (en) * | 1995-06-26 | 1997-07-15 | Eastman Kodak Company | Automated photofinishing apparatus |
US5664253A (en) * | 1995-09-12 | 1997-09-02 | Eastman Kodak Company | Stand alone photofinishing apparatus |
US5664255A (en) * | 1996-05-29 | 1997-09-02 | Eastman Kodak Company | Photographic printing and processing apparatus |
US5667944A (en) * | 1995-10-25 | 1997-09-16 | Eastman Kodak Company | Digital process sensitivity correction |
US5678116A (en) * | 1994-04-06 | 1997-10-14 | Dainippon Screen Mfg. Co., Ltd. | Method and apparatus for drying a substrate having a resist film with a miniaturized pattern |
US5726773A (en) * | 1994-11-29 | 1998-03-10 | Carl-Zeiss-Stiftung | Apparatus for scanning and digitizing photographic image objects and method of operating said apparatus |
US5729631A (en) * | 1993-11-30 | 1998-03-17 | Polaroid Corporation | Image noise reduction system using a wiener variant filter in a pyramid image representation |
US5739897A (en) * | 1994-08-16 | 1998-04-14 | Gretag Imaging Ag | Method and system for creating index prints on and/or with a photographic printer |
US5771107A (en) * | 1995-01-11 | 1998-06-23 | Mita Industrial Co., Ltd. | Image processor with image edge emphasizing capability |
US5771318A (en) * | 1996-06-27 | 1998-06-23 | Siemens Corporate Research, Inc. | Adaptive edge-preserving smoothing filter |
US5790277A (en) * | 1994-06-08 | 1998-08-04 | International Business Machines Corporation | Duplex film scanning |
US5867606A (en) * | 1997-08-12 | 1999-02-02 | Hewlett-Packard Company | Apparatus and method for determining the appropriate amount of sharpening for an image |
US5870172A (en) * | 1996-03-29 | 1999-02-09 | Blume; Stephen T. | Apparatus for producing a video and digital image directly from dental x-ray film |
US5880819A (en) * | 1995-06-29 | 1999-03-09 | Fuji Photo Film Co., Ltd. | Photographic film loading method, photographic film conveying apparatus, and image reading apparatus |
US5892595A (en) * | 1996-01-26 | 1999-04-06 | Ricoh Company, Ltd. | Image reading apparatus for correct positioning of color component values of each picture element |
US5930388A (en) * | 1996-10-24 | 1999-07-27 | Sharp Kabuskiki Kaisha | Color image processing apparatus |
US5959720A (en) * | 1996-03-22 | 1999-09-28 | Eastman Kodak Company | Method for color balance determination |
US6065824A (en) * | 1994-12-22 | 2000-05-23 | Hewlett-Packard Company | Method and apparatus for storing information on a replaceable ink container |
US6069714A (en) * | 1996-12-05 | 2000-05-30 | Applied Science Fiction, Inc. | Method and apparatus for reducing noise in electronic film development |
US6088084A (en) * | 1997-10-17 | 2000-07-11 | Fuji Photo Film Co., Ltd. | Original carrier and image reader |
US6089687A (en) * | 1998-03-09 | 2000-07-18 | Hewlett-Packard Company | Method and apparatus for specifying ink volume in an ink container |
US6101273A (en) * | 1995-10-31 | 2000-08-08 | Fuji Photo Film Co., Ltd. | Image reproducing method and apparatus |
US6102508A (en) * | 1996-09-27 | 2000-08-15 | Hewlett-Packard Company | Method and apparatus for selecting printer consumables |
US6200738B1 (en) * | 1998-10-29 | 2001-03-13 | Konica Corporation | Image forming method |
US6370279B1 (en) * | 1997-04-10 | 2002-04-09 | Samsung Electronics Co., Ltd. | Block-based image processing method and apparatus therefor |
US6707940B1 (en) * | 2000-03-31 | 2004-03-16 | Intel Corporation | Method and apparatus for image segmentation |
Family Cites Families (32)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
BE682559A (en) | 1965-06-16 | 1966-11-14 | ||
US3617282A (en) | 1970-05-18 | 1971-11-02 | Eastman Kodak Co | Nucleating agents for photographic reversal processes |
JPS5459343A (en) * | 1977-10-20 | 1979-05-12 | Green Cross Corp | Food additives for supplyng food and feed being defficient from fiber substance |
US4301469A (en) | 1980-04-30 | 1981-11-17 | United Technologies Corporation | Run length encoder for color raster scanner |
US4490729A (en) | 1982-09-15 | 1984-12-25 | The Mead Corporation | Ink jet printer |
US4607779A (en) * | 1983-08-11 | 1986-08-26 | National Semiconductor Corporation | Non-impact thermocompression gang bonding method |
JPS6089723A (en) | 1983-10-21 | 1985-05-20 | Canon Inc | Color information detector |
DE3581010D1 (en) | 1984-07-09 | 1991-02-07 | Sigma Corp | DEVELOPMENT END POINT PROCEDURE. |
US4623236A (en) | 1985-10-31 | 1986-11-18 | Polaroid Corporation | Photographic processing composition applicator |
AU609610B2 (en) | 1988-05-20 | 1991-05-02 | Sanyo Electric Co., Ltd. | Image sensing apparatus having automatic iris function of automatically adjusting exposure in response to video signal |
US5267030A (en) | 1989-12-22 | 1993-11-30 | Eastman Kodak Company | Method and associated apparatus for forming image data metrics which achieve media compatibility for subsequent imaging application |
GB9100194D0 (en) | 1991-01-05 | 1991-02-20 | Ilford Ltd | Roll film assembly |
US5081692A (en) * | 1991-04-04 | 1992-01-14 | Eastman Kodak Company | Unsharp masking using center weighted local variance for image sharpening and noise suppression |
JP2654284B2 (en) | 1991-10-03 | 1997-09-17 | 富士写真フイルム株式会社 | Photo print system |
JP2936085B2 (en) * | 1991-11-19 | 1999-08-23 | 富士写真フイルム株式会社 | Image data processing method and apparatus |
US5266805A (en) | 1992-05-05 | 1993-11-30 | International Business Machines Corporation | System and method for image recovery |
US5371542A (en) | 1992-06-23 | 1994-12-06 | The United States Of America As Represented By The Secretary Of The Navy | Dual waveband signal processing system |
CA2093840C (en) | 1992-07-17 | 1999-08-10 | Albert D. Edgar | Duplex film scanning |
JPH07125902A (en) | 1993-10-29 | 1995-05-16 | Minolta Co Ltd | Image printer |
US5477345A (en) | 1993-12-15 | 1995-12-19 | Xerox Corporation | Apparatus for subsampling chrominance |
JPH07274004A (en) * | 1994-03-29 | 1995-10-20 | Dainippon Screen Mfg Co Ltd | Sharpness emphasizing device for picture |
JPH0877341A (en) | 1994-08-29 | 1996-03-22 | Xerox Corp | Equipment and method for color image processing |
US5587752A (en) | 1995-06-05 | 1996-12-24 | Eastman Kodak Company | Camera, system and method for producing composite photographic image |
US5695914A (en) | 1995-09-15 | 1997-12-09 | Eastman Kodak Company | Process of forming a dye image |
US5698382A (en) | 1995-09-25 | 1997-12-16 | Konica Corporation | Processing method for silver halide color photographic light-sensitive material |
US5845007A (en) * | 1996-01-02 | 1998-12-01 | Cognex Corporation | Machine vision method and apparatus for edge-based image histogram analysis |
AU727503B2 (en) * | 1996-07-31 | 2000-12-14 | Canon Kabushiki Kaisha | Image filtering method and apparatus |
US5691118A (en) | 1996-10-10 | 1997-11-25 | Eastman Kodak Company | Color paper processing using two acidic stop solutions before and after bleaching |
EP0917347A3 (en) * | 1997-11-17 | 2000-12-13 | Xerox Corporation | Dynamically adjustable unsharp masking for digital image processing |
US7016080B2 (en) * | 2000-09-21 | 2006-03-21 | Eastman Kodak Company | Method and system for improving scanned image detail |
JP4281311B2 (en) * | 2001-09-11 | 2009-06-17 | セイコーエプソン株式会社 | Image processing using subject information |
DE60234802D1 (en) * | 2001-10-10 | 2010-02-04 | Applied Materials Israel Ltd | Method and apparatus for automatic imaging suitable for aligning a charged particle beam column |
-
2001
- 2001-09-21 US US09/960,239 patent/US7016080B2/en not_active Expired - Fee Related
- 2001-09-21 AU AU2001294669A patent/AU2001294669A1/en not_active Abandoned
- 2001-09-21 JP JP2002529010A patent/JP2004517384A/en active Pending
- 2001-09-21 US US09/960,276 patent/US20020176113A1/en not_active Abandoned
- 2001-09-21 CN CNA018191533A patent/CN1474997A/en active Pending
- 2001-09-21 TW TW090123325A patent/TW538382B/en not_active IP Right Cessation
- 2001-09-21 WO PCT/US2001/029833 patent/WO2002025928A2/en active Application Filing
- 2001-09-21 EP EP01975333A patent/EP1323292A2/en not_active Withdrawn
Patent Citations (99)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US2404138A (en) * | 1941-10-06 | 1946-07-16 | Alvin L Mayer | Apparatus for developing exposed photographic prints |
US3250689A (en) * | 1965-05-03 | 1966-05-10 | Robert G Seyl | Simplified method of measuring corrosion using reference electrode |
US3520690A (en) * | 1965-06-25 | 1970-07-14 | Fuji Photo Film Co Ltd | Process for controlling dye gradation in color photographic element |
US3615498A (en) * | 1967-07-29 | 1971-10-26 | Fuji Photo Film Co Ltd | Color developers containing substituted nbenzyl-p-aminophenol competing developing agents |
US3615479A (en) * | 1968-05-27 | 1971-10-26 | Itek Corp | Automatic film processing method and apparatus therefor |
US3587435A (en) * | 1969-04-24 | 1971-06-28 | Pat P Chioffe | Film processing machine |
US3946398A (en) * | 1970-06-29 | 1976-03-23 | Silonics, Inc. | Method and apparatus for recording with writing fluids and drop projection means therefor |
US3747120A (en) * | 1971-01-11 | 1973-07-17 | N Stemme | Arrangement of writing mechanisms for writing on paper with a coloredliquid |
US3903541A (en) * | 1971-07-27 | 1975-09-02 | Meister Frederick W Von | Apparatus for processing printing plates precoated on one side only |
US3833161A (en) * | 1972-02-08 | 1974-09-03 | Bosch Photokino Gmbh | Apparatus for intercepting and threading the leader of convoluted motion picture film or the like |
US4081577A (en) * | 1973-12-26 | 1978-03-28 | American Hoechst Corporation | Pulsed spray of fluids |
US3959048A (en) * | 1974-11-29 | 1976-05-25 | Stanfield James S | Apparatus and method for repairing elongated flexible strips having damaged sprocket feed holes along the edge thereof |
US4026756A (en) * | 1976-03-19 | 1977-05-31 | Stanfield James S | Apparatus for repairing elongated flexible strips having damaged sprocket feed holes along the edge thereof |
US4745040A (en) * | 1976-08-27 | 1988-05-17 | Levine Alfred B | Method for destructive electronic development of photo film |
US4777102A (en) * | 1976-08-27 | 1988-10-11 | Levine Alfred B | Method and apparatus for electronic development of color photographic film |
US4142107A (en) * | 1977-06-30 | 1979-02-27 | International Business Machines Corporation | Resist development control system |
US4249985A (en) * | 1979-03-05 | 1981-02-10 | Stanfield James S | Pressure roller for apparatus useful in repairing sprocket holes on strip material |
US4215927A (en) * | 1979-04-13 | 1980-08-05 | Scott Paper Company | Lithographic plate processing apparatus |
US4265545A (en) * | 1979-07-27 | 1981-05-05 | Intec Corporation | Multiple source laser scanning inspection system |
US4501480A (en) * | 1981-10-16 | 1985-02-26 | Pioneer Electronic Corporation | System for developing a photo-resist material used as a recording medium |
US4594598A (en) * | 1982-10-26 | 1986-06-10 | Sharp Kabushiki Kaisha | Printer head mounting assembly in an ink jet system printer |
US4564280A (en) * | 1982-10-28 | 1986-01-14 | Fujitsu Limited | Method and apparatus for developing resist film including a movable nozzle arm |
US4670779A (en) * | 1984-01-10 | 1987-06-02 | Sharp Kabushiki Kaisha | Color-picture analyzing apparatus with red-purpose and green-purpose filters |
US4666307A (en) * | 1984-01-19 | 1987-05-19 | Fuji Photo Film Co., Ltd. | Method for calibrating photographic image information |
US4755844A (en) * | 1985-04-30 | 1988-07-05 | Kabushiki Kaisha Toshiba | Automatic developing device |
US4845551A (en) * | 1985-05-31 | 1989-07-04 | Fuji Photo Film Co., Ltd. | Method for correcting color photographic image data on the basis of calibration data read from a reference film |
US4636808A (en) * | 1985-09-09 | 1987-01-13 | Eastman Kodak Company | Continuous ink jet printer |
US4736221A (en) * | 1985-10-18 | 1988-04-05 | Fuji Photo Film Co., Ltd. | Method and device for processing photographic film using atomized liquid processing agents |
US4796061A (en) * | 1985-11-16 | 1989-01-03 | Dainippon Screen Mfg. Co., Ltd. | Device for detachably attaching a film onto a drum in a drum type picture scanning recording apparatus |
US4821114A (en) * | 1986-05-02 | 1989-04-11 | Dr. Ing. Rudolf Hell Gmbh | Opto-electronic scanning arrangement |
US4741621A (en) * | 1986-08-18 | 1988-05-03 | Westinghouse Electric Corp. | Geometric surface inspection system with dual overlap light stripe generator |
US4814630A (en) * | 1987-06-29 | 1989-03-21 | Ncr Corporation | Document illuminating apparatus using light sources A, B, and C in periodic arrays |
US4875067A (en) * | 1987-07-23 | 1989-10-17 | Fuji Photo Film Co., Ltd. | Processing apparatus |
US5034767A (en) * | 1987-08-28 | 1991-07-23 | Hanetz International Inc. | Development system |
US4857430A (en) * | 1987-12-17 | 1989-08-15 | Texas Instruments Incorporated | Process and system for determining photoresist development endpoint by effluent analysis |
US4851311A (en) * | 1987-12-17 | 1989-07-25 | Texas Instruments Incorporated | Process for determining photoresist develop time by optical transmission |
US4994918A (en) * | 1989-04-28 | 1991-02-19 | Bts Broadcast Television Systems Gmbh | Method and circuit for the automatic correction of errors in image steadiness during film scanning |
US5027146A (en) * | 1989-08-31 | 1991-06-25 | Eastman Kodak Company | Processing apparatus |
US5101286A (en) * | 1990-03-21 | 1992-03-31 | Eastman Kodak Company | Scanning film during the film process for output to a video monitor |
US5196285A (en) * | 1990-05-18 | 1993-03-23 | Xinix, Inc. | Method for control of photoresist develop processes |
US5292605A (en) * | 1990-05-18 | 1994-03-08 | Xinix, Inc. | Method for control of photoresist develop processes |
US5124216A (en) * | 1990-07-31 | 1992-06-23 | At&T Bell Laboratories | Method for monitoring photoresist latent images |
US5231439A (en) * | 1990-08-03 | 1993-07-27 | Fuji Photo Film Co., Ltd. | Photographic film handling method |
US5416550A (en) * | 1990-09-14 | 1995-05-16 | Eastman Kodak Company | Photographic processing apparatus |
US5212512A (en) * | 1990-11-30 | 1993-05-18 | Fuji Photo Film Co., Ltd. | Photofinishing system |
US5155596A (en) * | 1990-12-03 | 1992-10-13 | Eastman Kodak Company | Film scanner illumination system having an automatic light control |
US5296923A (en) * | 1991-01-09 | 1994-03-22 | Konica Corporation | Color image reproducing device and method |
US5452018A (en) * | 1991-04-19 | 1995-09-19 | Sony Electronics Inc. | Digital color correction system having gross and fine adjustment modes |
US5391443A (en) * | 1991-07-19 | 1995-02-21 | Eastman Kodak Company | Process for the extraction of spectral image records from dye image forming photographic elements |
US5334247A (en) * | 1991-07-25 | 1994-08-02 | Eastman Kodak Company | Coater design for low flowrate coating applications |
US5235352A (en) * | 1991-08-16 | 1993-08-10 | Compaq Computer Corporation | High density ink jet printhead |
US5200817A (en) * | 1991-08-29 | 1993-04-06 | Xerox Corporation | Conversion of an RGB color scanner into a colorimetric scanner |
US5436738A (en) * | 1992-01-22 | 1995-07-25 | Eastman Kodak Company | Three dimensional thermal internegative photographic printing apparatus and method |
US5255408A (en) * | 1992-02-11 | 1993-10-26 | Eastman Kodak Company | Photographic film cleaner |
US5496669A (en) * | 1992-07-01 | 1996-03-05 | Interuniversitair Micro-Elektronica Centrum Vzw | System for detecting a latent image using an alignment apparatus |
US5519510A (en) * | 1992-07-17 | 1996-05-21 | International Business Machines Corporation | Electronic film development |
US5418597A (en) * | 1992-09-14 | 1995-05-23 | Eastman Kodak Company | Clamping arrangement for film scanning apparatus |
US5447811A (en) * | 1992-09-24 | 1995-09-05 | Eastman Kodak Company | Color image reproduction of scenes with preferential tone mapping |
US5357307A (en) * | 1992-11-25 | 1994-10-18 | Eastman Kodak Company | Apparatus for processing photosensitive material |
US5568270A (en) * | 1992-12-09 | 1996-10-22 | Fuji Photo Film Co., Ltd. | Image reading apparatus which varies reading time according to image density |
US5350651A (en) * | 1993-02-12 | 1994-09-27 | Eastman Kodak Company | Methods for the retrieval and differentiation of blue, green and red exposure records of the same hue from photographic elements containing absorbing interlayers |
US5350664A (en) * | 1993-02-12 | 1994-09-27 | Eastman Kodak Company | Photographic elements for producing blue, green, and red exposure records of the same hue and methods for the retrieval and differentiation of the exposure records |
US5546477A (en) * | 1993-03-30 | 1996-08-13 | Klics, Inc. | Data compression and decompression |
US5596415A (en) * | 1993-06-14 | 1997-01-21 | Eastman Kodak Company | Iterative predictor-based detection of image frame locations |
US5414779A (en) * | 1993-06-14 | 1995-05-09 | Eastman Kodak Company | Image frame detection |
US5550566A (en) * | 1993-07-15 | 1996-08-27 | Media Vision, Inc. | Video capture expansion card |
US5418119A (en) * | 1993-07-16 | 1995-05-23 | Eastman Kodak Company | Photographic elements for producing blue, green and red exposure records of the same hue |
US5448380A (en) * | 1993-07-31 | 1995-09-05 | Samsung Electronics Co., Ltd. | color image processing method and apparatus for correcting a color signal from an input image device |
US5440365A (en) * | 1993-10-14 | 1995-08-08 | Eastman Kodak Company | Photosensitive material processor |
US5729631A (en) * | 1993-11-30 | 1998-03-17 | Polaroid Corporation | Image noise reduction system using a wiener variant filter in a pyramid image representation |
US5552904A (en) * | 1994-01-31 | 1996-09-03 | Samsung Electronics Co., Ltd. | Color correction method and apparatus using adaptive region separation |
US5516608A (en) * | 1994-02-28 | 1996-05-14 | International Business Machines Corporation | Method for controlling a line dimension arising in photolithographic processes |
US5678116A (en) * | 1994-04-06 | 1997-10-14 | Dainippon Screen Mfg. Co., Ltd. | Method and apparatus for drying a substrate having a resist film with a miniaturized pattern |
US5790277A (en) * | 1994-06-08 | 1998-08-04 | International Business Machines Corporation | Duplex film scanning |
US5739897A (en) * | 1994-08-16 | 1998-04-14 | Gretag Imaging Ag | Method and system for creating index prints on and/or with a photographic printer |
US5726773A (en) * | 1994-11-29 | 1998-03-10 | Carl-Zeiss-Stiftung | Apparatus for scanning and digitizing photographic image objects and method of operating said apparatus |
US6065824A (en) * | 1994-12-22 | 2000-05-23 | Hewlett-Packard Company | Method and apparatus for storing information on a replaceable ink container |
US5771107A (en) * | 1995-01-11 | 1998-06-23 | Mita Industrial Co., Ltd. | Image processor with image edge emphasizing capability |
US5563717A (en) * | 1995-02-03 | 1996-10-08 | Eastman Kodak Company | Method and means for calibration of photographic media using pre-exposed miniature images |
US5649260A (en) * | 1995-06-26 | 1997-07-15 | Eastman Kodak Company | Automated photofinishing apparatus |
US5880819A (en) * | 1995-06-29 | 1999-03-09 | Fuji Photo Film Co., Ltd. | Photographic film loading method, photographic film conveying apparatus, and image reading apparatus |
US5664253A (en) * | 1995-09-12 | 1997-09-02 | Eastman Kodak Company | Stand alone photofinishing apparatus |
US5667944A (en) * | 1995-10-25 | 1997-09-16 | Eastman Kodak Company | Digital process sensitivity correction |
US6101273A (en) * | 1995-10-31 | 2000-08-08 | Fuji Photo Film Co., Ltd. | Image reproducing method and apparatus |
US5892595A (en) * | 1996-01-26 | 1999-04-06 | Ricoh Company, Ltd. | Image reading apparatus for correct positioning of color component values of each picture element |
US5627016A (en) * | 1996-02-29 | 1997-05-06 | Eastman Kodak Company | Method and apparatus for photofinishing photosensitive film |
US5959720A (en) * | 1996-03-22 | 1999-09-28 | Eastman Kodak Company | Method for color balance determination |
US5870172A (en) * | 1996-03-29 | 1999-02-09 | Blume; Stephen T. | Apparatus for producing a video and digital image directly from dental x-ray film |
US5664255A (en) * | 1996-05-29 | 1997-09-02 | Eastman Kodak Company | Photographic printing and processing apparatus |
US5771318A (en) * | 1996-06-27 | 1998-06-23 | Siemens Corporate Research, Inc. | Adaptive edge-preserving smoothing filter |
US6102508A (en) * | 1996-09-27 | 2000-08-15 | Hewlett-Packard Company | Method and apparatus for selecting printer consumables |
US5930388A (en) * | 1996-10-24 | 1999-07-27 | Sharp Kabuskiki Kaisha | Color image processing apparatus |
US6069714A (en) * | 1996-12-05 | 2000-05-30 | Applied Science Fiction, Inc. | Method and apparatus for reducing noise in electronic film development |
US6370279B1 (en) * | 1997-04-10 | 2002-04-09 | Samsung Electronics Co., Ltd. | Block-based image processing method and apparatus therefor |
US5867606A (en) * | 1997-08-12 | 1999-02-02 | Hewlett-Packard Company | Apparatus and method for determining the appropriate amount of sharpening for an image |
US6088084A (en) * | 1997-10-17 | 2000-07-11 | Fuji Photo Film Co., Ltd. | Original carrier and image reader |
US6089687A (en) * | 1998-03-09 | 2000-07-18 | Hewlett-Packard Company | Method and apparatus for specifying ink volume in an ink container |
US6200738B1 (en) * | 1998-10-29 | 2001-03-13 | Konica Corporation | Image forming method |
US6707940B1 (en) * | 2000-03-31 | 2004-03-16 | Intel Corporation | Method and apparatus for image segmentation |
Cited By (61)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7016080B2 (en) * | 2000-09-21 | 2006-03-21 | Eastman Kodak Company | Method and system for improving scanned image detail |
US20020103006A1 (en) * | 2001-01-31 | 2002-08-01 | Steven Doe | Liquid crystal display device |
US20040066458A1 (en) * | 2002-07-12 | 2004-04-08 | Hiroyuki Kawamura | Imaging system |
US20040051794A1 (en) * | 2002-09-12 | 2004-03-18 | Pentax Corporation | Filter process |
US7683944B2 (en) * | 2002-09-12 | 2010-03-23 | Hoya Corporation | Filter process for obtaining a soft focus picture image |
EP1443459A2 (en) * | 2003-02-03 | 2004-08-04 | Noritsu Koki Co., Ltd. | Image processing method and apparatus for correcting photographic images |
US20040184672A1 (en) * | 2003-02-03 | 2004-09-23 | Kenji Murakami | Image processing method and apparatus for correcting photographic images |
EP1443459A3 (en) * | 2003-02-03 | 2004-09-29 | Noritsu Koki Co., Ltd. | Image processing method and apparatus for correcting photographic images |
US20050057484A1 (en) * | 2003-09-15 | 2005-03-17 | Diefenbaugh Paul S. | Automatic image luminance control with backlight adjustment |
US7397964B2 (en) * | 2004-06-24 | 2008-07-08 | Apple Inc. | Gaussian blur approximation suitable for GPU |
US20050286794A1 (en) * | 2004-06-24 | 2005-12-29 | Apple Computer, Inc. | Gaussian blur approximation suitable for GPU |
US20060182363A1 (en) * | 2004-12-21 | 2006-08-17 | Vladimir Jellus | Method for correcting inhomogeneities in an image, and an imaging apparatus therefor |
US7672498B2 (en) * | 2004-12-21 | 2010-03-02 | Siemens Aktiengesellschaft | Method for correcting inhomogeneities in an image, and an imaging apparatus therefor |
US20060232823A1 (en) * | 2005-04-13 | 2006-10-19 | Hooper David S | Image contrast enhancement |
US8228560B2 (en) | 2005-04-13 | 2012-07-24 | Acd Systems International Inc. | Image contrast enhancement |
US20070036456A1 (en) * | 2005-04-13 | 2007-02-15 | Hooper David S | Image contrast enhancement |
US8014034B2 (en) | 2005-04-13 | 2011-09-06 | Acd Systems International Inc. | Image contrast enhancement |
US8928947B2 (en) | 2005-04-13 | 2015-01-06 | Acd Systems International Inc. | Image contrast enhancement |
US20060285164A1 (en) * | 2005-06-21 | 2006-12-21 | Chun-Yi Wang | Method for Processing Multi-layered Image Data |
US7769244B2 (en) * | 2005-09-05 | 2010-08-03 | Algosoft-Tech Usa, Llc. | Automatic digital film and video restoration |
US20080170801A1 (en) * | 2005-09-05 | 2008-07-17 | Algosoft Limited | Automatic digital film and video restoration |
US8625885B2 (en) | 2006-03-23 | 2014-01-07 | Intelliscience Corporation | Methods and systems for data analysis and feature recognition |
US20100017353A1 (en) * | 2006-03-23 | 2010-01-21 | Intelliscience Corporation | Methods and systems for data analysis and feature recognition |
US20080031548A1 (en) * | 2006-03-23 | 2008-02-07 | Intelliscience Corporation | Systems and methods for data transformation |
US20070244844A1 (en) * | 2006-03-23 | 2007-10-18 | Intelliscience Corporation | Methods and systems for data analysis and feature recognition |
US20080033984A1 (en) * | 2006-04-10 | 2008-02-07 | Intelliscience Corporation | Systems and methods for data point processing |
WO2008022222A3 (en) * | 2006-08-15 | 2008-08-21 | Intelliscience Corp | Systems and methods for data transformation |
WO2008022222A2 (en) * | 2006-08-15 | 2008-02-21 | Intelliscience Corporation | Systems and methods for data transformation |
US8175992B2 (en) | 2008-03-17 | 2012-05-08 | Intelliscience Corporation | Methods and systems for compound feature creation, processing, and identification in conjunction with a data analysis and feature recognition system wherein hit weights are summed |
US8400280B2 (en) * | 2008-05-27 | 2013-03-19 | Samsung Electronics Co., Ltd. | Display tag, display tag system having display tag, and method for writing display tag information |
US20090295549A1 (en) * | 2008-05-27 | 2009-12-03 | Samsung Electronics Co., Ltd. | Display tag, display tag system having display tag, and method for writing display tag information |
CN101593267A (en) * | 2008-05-27 | 2009-12-02 | 三星电子株式会社 | The method of display label, display tag system and writing display tag information |
US9305337B2 (en) | 2008-09-04 | 2016-04-05 | Lattice Semiconductor Corporation | System, method, and apparatus for smoothing of edges in images to remove irregularities |
US8559746B2 (en) | 2008-09-04 | 2013-10-15 | Silicon Image, Inc. | System, method, and apparatus for smoothing of edges in images to remove irregularities |
US20100202262A1 (en) * | 2009-02-10 | 2010-08-12 | Anchor Bay Technologies, Inc. | Block noise detection and filtering |
US8452117B2 (en) * | 2009-02-10 | 2013-05-28 | Silicon Image, Inc. | Block noise detection and filtering |
US8891897B2 (en) | 2009-02-10 | 2014-11-18 | Silicon Image, Inc. | Block noise detection and filtering |
US8274583B2 (en) | 2009-06-05 | 2012-09-25 | Apple Inc. | Radially-based chroma noise reduction for cameras |
US8284271B2 (en) * | 2009-06-05 | 2012-10-09 | Apple Inc. | Chroma noise reduction for cameras |
US20100309345A1 (en) * | 2009-06-05 | 2010-12-09 | Apple Inc. | Radially-Based Chroma Noise Reduction for Cameras |
US20100309344A1 (en) * | 2009-06-05 | 2010-12-09 | Apple Inc. | Chroma noise reduction for cameras |
US20110242367A1 (en) * | 2010-03-31 | 2011-10-06 | Samsung Electronics Co., Ltd. | Image processing method and photographing apparatus using the same |
US8681244B2 (en) * | 2010-03-31 | 2014-03-25 | Samsung Electronics Co., Ltd | Image processing method using blurring and photographing apparatus using the same |
WO2013078182A1 (en) * | 2011-11-21 | 2013-05-30 | Georgetown University | System and method for enhancing the legibility of degraded images |
US8995782B2 (en) | 2011-11-21 | 2015-03-31 | Georgetown University | System and method for enhancing the legibility of degraded images |
US9361676B2 (en) | 2011-11-21 | 2016-06-07 | Georgetown University | System and method for enhancing the legibility of degraded images |
KR101683689B1 (en) | 2011-12-16 | 2016-12-07 | 지멘스 악티엔게젤샤프트 | Method to create an mr image of an examination subject with a magnetic resonance system, as well as a corresponding magnetic resonance system |
KR20130069494A (en) * | 2011-12-16 | 2013-06-26 | 지멘스 악티엔게젤샤프트 | Method to create an mr image of an examination subject with a magnetic resonance system, as well as a corresponding magnetic resonance system |
US9297871B2 (en) | 2011-12-16 | 2016-03-29 | Siemens Aktiengesellschaft | Magnetic resonance system and method to generate a magnetic resonance image of an examination subject |
US20130230244A1 (en) * | 2012-03-02 | 2013-09-05 | Chintan Intwala | Continuously Adjustable Bleed for Selected Region Blurring |
US9019310B2 (en) | 2012-03-02 | 2015-04-28 | Adobe Systems Incorporated | Methods and apparatus for applying complex continuous gradients to images |
US8693776B2 (en) * | 2012-03-02 | 2014-04-08 | Adobe Systems Incorporated | Continuously adjustable bleed for selected region blurring |
US8831371B2 (en) | 2012-03-02 | 2014-09-09 | Adobe Systems Incorporated | Methods and apparatus for applying blur patterns to images |
US8824793B2 (en) | 2012-03-02 | 2014-09-02 | Adobe Systems Incorporated | Methods and apparatus for applying a bokeh effect to images |
CN103679656A (en) * | 2013-10-21 | 2014-03-26 | 厦门美图网科技有限公司 | Automatic image sharpening method |
US20160078601A1 (en) * | 2014-09-12 | 2016-03-17 | Tmm, Inc. | Image upsampling using local adaptive weighting |
US9600868B2 (en) * | 2014-09-12 | 2017-03-21 | Tmm, Inc. | Image upsampling using local adaptive weighting |
US10510153B1 (en) * | 2017-06-26 | 2019-12-17 | Amazon Technologies, Inc. | Camera-level image processing |
US10580149B1 (en) * | 2017-06-26 | 2020-03-03 | Amazon Technologies, Inc. | Camera-level image processing |
US20230200639A1 (en) * | 2021-12-27 | 2023-06-29 | Novasight Ltd. | Method and device for treating / preventing refractive errors as well as for image processing and display |
US11918287B2 (en) * | 2021-12-27 | 2024-03-05 | Novasight Ltd. | Method and device for treating / preventing refractive errors as well as for image processing and display |
Also Published As
Publication number | Publication date |
---|---|
AU2001294669A1 (en) | 2002-04-02 |
JP2004517384A (en) | 2004-06-10 |
US7016080B2 (en) | 2006-03-21 |
WO2002025928A2 (en) | 2002-03-28 |
EP1323292A2 (en) | 2003-07-02 |
US20020126327A1 (en) | 2002-09-12 |
WO2002025928A3 (en) | 2003-01-16 |
CN1474997A (en) | 2004-02-11 |
TW538382B (en) | 2003-06-21 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20020176113A1 (en) | Dynamic image correction and imaging systems | |
US6822762B2 (en) | Local color correction | |
EP0398861B1 (en) | Method for adaptively sharpening electronic images | |
US8363123B2 (en) | Image pickup apparatus, color noise reduction method, and color noise reduction program | |
Mann | Comparametric equations with practical applications in quantigraphic image processing | |
US7302110B2 (en) | Image enhancement methods and apparatus therefor | |
US7068853B2 (en) | Tone scale adjustment of digital images | |
US6366318B1 (en) | CFA correction for CFA images captured at partial resolution | |
US7769241B2 (en) | Method of sharpening using panchromatic pixels | |
US6792160B2 (en) | General purpose image enhancement algorithm which augments the visual perception of detail in digital images | |
EP1139284B1 (en) | Method and apparatus for performing local color correction | |
JPH0922460A (en) | Image processing method and device therefor | |
JP2003134352A (en) | Image processing method and apparatus, and program therefor | |
JP2007096509A (en) | Image processing apparatus and image processing method | |
JP4600424B2 (en) | Development processing apparatus for undeveloped image data, development processing method, and computer program for development processing | |
JP2011228807A (en) | Image processing program, image processing apparatus, and image processing method | |
JPH10214339A (en) | Picture filtering method | |
US20060056722A1 (en) | Edge preserving method and apparatus for image processing | |
US6384937B1 (en) | Image processing method and apparatus | |
JP3729118B2 (en) | Image processing method, image processing apparatus, image processing program, and computer-readable recording medium recording the same | |
JP2020145553A (en) | Image processing apparatus, image processing method and program | |
JP4032200B2 (en) | Image data interpolation method, image data interpolation device, and computer readable recording medium recording image data interpolation program | |
US5633734A (en) | Method and apparatus for modifying a fluorescent portion of a digital image | |
JPH08223425A (en) | Image processing method and its device | |
JP4091220B2 (en) | Image processing method and apparatus, and recording medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: APPLIED SCIENCE FICTION, INC., TEXAS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:EDGAR, ALBERT D.;REEL/FRAME:012562/0417 Effective date: 20011115 |
|
AS | Assignment |
Owner name: RHO VENTURES (QP), L.P., NEW YORK Free format text: SECURITY INTEREST;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:012997/0113 Effective date: 20020723 Owner name: CENTERPOINT VENTURE PARTNERS, L.P., TEXAS Free format text: SECURITY AGREEMENT;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:012997/0211 Effective date: 20020723 Owner name: CENTERPOINT VENTURE PARTNERS, L.P., TEXAS Free format text: SECURITY INTEREST;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:012997/0113 Effective date: 20020723 Owner name: RHO VENTURES (QP), L.P., NEW YORK Free format text: SECURITY AGREEMENT;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:012997/0211 Effective date: 20020723 |
|
AS | Assignment |
Owner name: CENTERPOINT VENTURE PARTNERS, L.P., TEXAS Free format text: SECURITY AGREEMENT;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:013506/0065 Effective date: 20030213 Owner name: RHO VENTURES (QP), L.P., NEW YORK Free format text: SECURITY AGREEMENT;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:013506/0065 Effective date: 20030213 |
|
AS | Assignment |
Owner name: EASTMAN KODAK COMPANY, NEW YORK Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:APPLIED SCIENCE FICTION, INC.;REEL/FRAME:014293/0774 Effective date: 20030521 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |