Patent Publication Number: US-10785466-B2

Title: Multi-image color-refinement with application to disparity estimation

Description:
BACKGROUND 
     This disclosure relates generally to the field of image processing. More particularly, but not by way of limitation, it relates to a technique for improving disparity estimation operations by incorporating color-refinement estimation therein. 
     The process of estimating the depth of a scene from two cameras is commonly referred to as stereoscopic vision and, when using multiple cameras, multi-view stereo. In practice, many multi-camera systems use disparity as a proxy for depth. (As used herein, disparity is taken to mean the difference in the projected location of a scene point in one image compared to that same point in another image captured by a different camera.) With a geometrically calibrated camera system, disparity can be mapped onto scene depth. The fundamental task for such multi-camera vision-based depth estimation systems then is to find matches, or correspondences, of points between images from two or more cameras. Using geometric calibration, the correspondences of a point in a reference image (A) can be shown to lie along a certain line, curve or path in another image (B). 
     Typically image noise, differences in precise color calibration of each camera, and other factors can lead to multiple possible matches and incorrect matches when considering only single points (i.e., pixels). For this reason, many known matching techniques use image patches or neighborhoods to compare the region around a point in image A with the region around a candidate point in image B. Simply comparing a whole patch rather than a sampled pixel value can mitigate noise, but not color biases from one image to another such as are present between almost any two different sensors. 
     Methods such as Normalized Cross-Correlation (NCC) or Census transform can obtain better matches of image features when there are color or lighting changes between the images. While these approaches provide improved matching, they do so at the cost of filtering and discarding some of the original images&#39; intrinsic information: namely areas of limited texture where there is still a slow gradient (e.g., a slow change in color or intensity). For example, a transition from light to dark across a large flat wall in a scene will be transformed by these methods so as to contain little matching information except at the area&#39;s edges. With either pixel-wise or patch-based matching, gradually changing image areas also cannot normally be matched. 
     SUMMARY 
     In one embodiment the disclosed concepts provide a method to perform a multi-image color-refinement and disparity map generation operation. The method includes obtaining first and second input images of a scene. Each input image comprising pixels, each pixel comprising a color value, each pixel in the first input image having a corresponding pixel in the second input image, and where the first and second input images were captured at substantially the same time. From the first and second input images a disparity map may be found and then used to register the two images. One or more pixels in the first input image may then be adjusted (based on the color value of the corresponding pixels in the second input image) to generate a color-refined image, where each pixel in the color-refined image has a corresponding pixel in the second input image. The combined actions of finding, registering, and adjusting may then be performed two or more additional times using the color-refined image and the second input image as the first and second input images respectively, where each of the additional times result in a new disparity map and a new color-refined image. Each new color-refined image and second image are used as the first and second input images respectively for a subsequent finding, registering, and adjusting combination. The color-refined image resulting from the last time the combined actions of finding, registering, and adjusting were performed may be stored in memory. In another embodiment, the disparity map resulting from the last time the combined actions of finding, registering, and adjusting were performed may be stored in memory. In yet another embodiment, both the last mentioned color-refined image and disparity map may be stored in memory. In one embodiment the first and second input images may be obtained from a high dynamic range image capture operation. In another embodiment, the first and second input images may be obtained from a stereoscopic (or multi-view) camera system. In still another embodiment, the first and second input images may be down-sampled (or, in general, transformed) versions of other images. Various implementations of the methods described herein may be embodied in devices (e.g., portable electronic devices incorporating a camera unit) and/or as computer executable instructions stored on a non-transitory program storage device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows, in flowchart form, a multi-image color-refinement operation in accordance with one embodiment. 
         FIG. 2  shows, in flowchart form, a color-refinement operation in accordance with one embodiment. 
         FIGS. 3A-3C  illustrate the use of spatial alignment and color-refinement of an image in accordance with one embodiment. 
         FIGS. 4A-4F  illustrate the use of spatial alignment and color-refinement of scan line images in accordance with one embodiment. 
         FIG. 5  shows, in block diagram form, a computer system in accordance with one embodiment. 
         FIG. 6  shows, in block diagram form, a multi-function electronic device in accordance with one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure pertains to systems, methods, and computer readable media to improve multi-image color-refinement operations. In general, techniques are disclosed for refining color differences between images in a multi-image camera system with application to disparity estimation. As used herein, the phrase “multi-image camera system” is taken to mean a camera system that captures two or more images—each from a different physical location—at substantially the same time. While such images may be captured by widely separated image capture devices (aka cameras), large physical separation is not needed. Recognizing that corresponding pixels between two (or more) images of a scene should have not only the same spatial location, but the same color, can be used to improve the spatial alignment of two (or more) such images and the generation of improved disparity maps. After making an initial disparity estimation and using it to align the images, colors in one image may be refined toward that of another of the captured images. (The image being color corrected may be either the reference image or the image(s) being registered with the reference image.) Repeating this process in an iterative manner allows improved spatial alignment between the images and the generation of superior disparity maps between the two (or more) images. 
     In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed concepts. As part of this description, some of this disclosure&#39;s drawings represent structures and devices in block diagram form in order to avoid obscuring the novel aspects of the disclosed concepts. In the interest of clarity, not all features of an actual implementation are described. Moreover, the language used in this disclosure has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter or resort to the claims being necessary to determine such inventive subject matter. Reference in this disclosure to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter, and multiple references to “one embodiment” or “an embodiment” should not be understood as necessarily all referring to the same embodiment. 
     It will be appreciated that in the development of any actual implementation (as in any software and/or hardware development project), numerous decisions must be made to achieve the developers&#39; specific goals (e.g., compliance with system-and business-related constraints), and that these goals may vary from one implementation to another. It will also be appreciated that such development efforts might be complex and time-consuming, but would nonetheless be a routine undertaking for those of ordinary skill in the design and implementation of graphics processing systems having the benefit of this disclosure. 
     Referring to  FIG. 1 , multi-image color-refinement operation  100  in accordance with one embodiment begins with input images A  105  and B  110 . For the purpose of this embodiment, image A  105  is treated as the reference image and the image being color corrected. (In another embodiment, image A  105  may be treated as the reference image while image B  110  is color corrected. In still another embodiment, image B  110  may be treated as the reference image while image A  105  is color corrected. In yet another embodiment, image B  110  may be treated as the reference image and be the image that is color corrected.) If the applied color correction is global (that is, the same across the image being color corrected), color adjustment may be made to either image. If the applied color correction is local (that is, different in different regions of the image being color corrected), it has been determined that adjustment to the reference image is often the easiest because it does not change coordinates. 
     From input images A  105  and B  110  an initial disparity estimate may be made to generate disparity map  120  (block  115 ). Operations in accordance with block  115  may estimate a match at each pixel or each patch of pixels in image B  110  with a corresponding pixel or patch of pixels in reference image A  105  using a given geometric calibration between the two cameras to limit the search to epipolar lines/curves/paths in image B  110  (one camera capturing image A  105 , a second camera capturing image B  110 ). In general, any color-or intensity-based disparity estimation algorithm operating on pixels or patches may be used. Disparity map  120  may then be used to register image B  110  to reference image A  105  to generate registered image B 2   130  (block  125 ). During registration, pixels in image B  110  may be warped so as to spatially align with corresponding pixels in reference image A  105 . In one embodiment, image B 2   130  may be formed by replacing each pixel in reference image A  105  by the pixel sampled from image B  110  at the coordinates corresponding to the best match to the original pixel (in image A  105 ) along the epipolar line, curve, or path—guided by or based on disparity map  120 . More specifically, consider a pixel at coordinate (i, j) in image B 2   130 : the location in image B  110  where this output pixel is sampled is determined by looking at pixel (i, j) in image A  105  and finding the best match along an epipolar path in image B  110 —yielding some other coordinate (i′, j′). To determine what constitutes a “best” match, any metric suitable to the target implementation may be used. Example techniques include, but are not limited to, normalized cross-correlation, sum of squared differences, and sum of absolute differences. The color of pixels in reference image A  105  may then be adjusted to better match the color of corresponding pixels in image B  110  to produce or generate color-refined image A′  140  (block  135 ). In one embodiment, this may be accomplished by any convenient or known image color-matching method. In another embodiment, a novel weighted non-linear color space warping approach may be used. (It will be recognized, various post-processing operations such as smoothing, filtering, or other regularization of the disparity map are also possible in addition to a matching procedure based solely on the best color/intensity match at each pixel as described here.) One illustrative implementation of such an approach is described below with respect to  FIG. 2 . With image A′  140 , a check may be made to determine if multi-image color-refinement operation  100  has reached completion (block  145 ). In one embodiment, operation  100  may complete after a specified number of iterations through blocks  115 - 135  (e.g., 5, 15, 17, 30). In another embodiment, operation  100  may be terminated when the magnitude of updates to image A  105  at block  135  fall below a specified threshold. In still another embodiment, operation  100  may be terminated when the changes introduced during block  135  from one iteration to the next fall below a specified threshold (e.g., an absolute value or a percentage change). If the adopted termination test is not successful (the “NO” prong of block  145 ), blocks  115 - 135  may be repeated treating color-refined image A′  140  as input image A at block  115 . If the adopted termination metric is successful (the “YES” prong of block  145 ), multi-image color-refinement operation  100  may be exited, yielding disparity map  120  and color-refined (reference) image A′  140 . 
     Operations in accordance with  FIG. 1  provide an iterative procedure that may enhance pixel-wise or patch-based matching without any transformations that attempt to normalize for color or lighting changes between the images. In accordance with operation  100 , at first only strong edge features may be matched correctly. As the color of image A is progressively refined through repeated iterations however, even areas of fairly low contrast may be matched via direct intensity/color-based matching. As a consequence, pixels in smoother areas may be matched which, in the prior art, would normally not provide any reliable matching information. Operations in accordance with  FIG. 1  also result in an improved/color-refined reference image (e.g., image A′  140 ) and a demonstrably better disparity map (e.g., map  120 ), both of which may be provided without the need for pre-computed calibration data such as is used in the prior art. It is noted that, while described with respect to two images, operations in accordance with  FIG. 1  may be extended to any number of images. By way of example, consider a three camera/image system resulting in a third image C. From image C a resulting image C 2  may be produced (corresponding to image B 2   130 ) and aligned to image A  105 . In this case, the color correction may be applied to each secondary image, i.e. image A may be left untouched and color-corrected images B 2 ′ and C 2 ′ may be produced (in lieu of image A′  140 ) and used as the input for each next iteration. 
     Referring to  FIG. 2 , in one embodiment, color adjustment operation  135  may be implemented as an automatic non-linear weighted color-refinement process. Initially, ‘k’ dominant color clusters in image B 2   130  may be found (block  200 ). In general, image B 2   130  may be thought of as being represented by a multidimensional color space and dominant color clusters may be identified using, for example, the k-means algorithm with random initialization. The clusters may be represented by a k-entry vector CB 2 ( i ), where ‘i’ runs from 1 to k and where CB 2 ( i ) represents the mean color of the points from image B 2   130  that are in the i th  color cluster. (Note, because each entry in vector CB 2  is a color, each entry may be a multi-dimensional element having a plurality of components, e.g., R, G, and B components.) Depending on a specific implementation&#39;s goals and/or operating environment other clustering techniques that may also be used include, but are not limited to, k-medians clustering, fuzzy C-means clustering, K q-flats, and self-organizing maps. In addition, actions in accordance with block  200  may or may not utilize pre-processing activity such as the canopy clustering algorithm to speed-up the ultimate clustering operation. With clusters defined or identified in accordance with block  200 , cluster index image IB 2  may be generated (block  205 ). As used here, cluster index image IB 2  comprises an image the same size as image B 2   130  and where each element in IB 2  contains a value indicative of image B 2 &#39;s corresponding pixel&#39;s associated cluster. In other words, the pixel at location (i, j) in image IB 2  contains the cluster index to which the pixel at location (i, j) in image B 2   130  belongs as determined during block  200 . 
     Processing image A  105  may begin with determining its color cluster vector (block  210 ). Color clusters for reference image A  105  may also be represented by a k-entry vector C A , where C A (i) represents the mean color value of those pixels in image A  105  corresponding to those entries in IB 2  whose value equals i. That is, for i=1 to k, I B2  acts like a mask where only those values in image A  105  corresponding to the selected cluster (as identified in image I B2 ) are selected to participate in calculating the i th  entry in color correction vector C A . A set of k distance images may then be generated (block  215 ):
 
 D ( i )=∥ A−C   B2 ( i )∥,  i= 1 to k  EQ. 1
 
where ∥ ∥ represents a distance operator so that D(i) is an image whose values are equal to the distance between each pixel of image A  105  and the i th  entry in image B 2 &#39;s associated color cluster image vector. In one embodiment, the distance identified in EQ. 1 may be calculated as a Euclidean distance in RGB space. Any distance metric relevant to the particular implementation may be adopted (e.g., a general Minkowski distance).
 
     Next, a set of ‘k’ weight images may be found (block  220 ):
 
 W ( i )= D ( i ) −1   , i= 1 to k,  EQ. 2
 
where W(i) represents the i th  weight image and corresponds to the i th  distance image. In one embodiment, each pixel in W(i) may be normalized by dividing its value by the sum of that pixel across all k images in W ( ) . As used here, the inverse is taken pixel-wise such that each element in distance image D(i) −1  is the reciprocal of the corresponding entry in image D(i). In practice, some regularization may also be used to control the smoothness of the weights. In one embodiment, for example:
 
 W ( i )=( D ( i )+∂) −n   ,i= 1to k,  EQ. 2A
 
where ∂ is a small constant that may be used to prevent the weights from growing too large, and ‘n’ could be a value greater than 1 (which will also affect the weighting function&#39;s smoothness). In another embodiment Gaussian weighting may be used:
 
 W ( i )=exp −D(i)     2     /s ,  EQ. 2B
 
where ‘s’ is a variance parameter, again controlling smoothness. In this context, smoothness may be thought of as being related to bandwidth and refers to how similar the color changes will be for different colors. For example, one smoothness function could make all colors darker, greener, etc. whereas a non-smooth function might make greens darker but cyans lighter. In general, the smoother a smoothing function is the wider its bandwidth.
 
     It has been unexpectedly found that by varying the distance metric (e.g., EQ. 1) and weighting metric (e.g., EQS. 2-2B) used, the bandwidth of the different colors across an image that affect a single sample of the output may be controlled. As used here, the terms “bandwidth” and “single sample of output” may be taken to mean the range of colors involved and one particular color in the output image&#39;s color palette (image A&#39;s) respectively. For instance, in the embodiment described herein, a narrow bandwidth could mean that for a particular shade of red in the image to be adjusted (image A  105 ), only similar nearby red-colored clusters in the input image (image B 2   130 ) would affect the color transformation to be applied to these reds in image A  105 . For a wide bandwidth, say pink colored pixel clusters in image B 2   130  might also have an influence on the color transformation to be applied to the red shades in image A  105 . For each cluster i the color difference vector E may be found (block  225 ):
 
 E ( i )= C   B2 ( i )− C   A ( i ), f or i=1 to k,  EQ. 3
 
where C B2  and C A  are as described above. The color distance vectors, in turn, may be used to determine k correction images (block  230 ):
 
 F ( i )= W ( i )* E ( i ),  EQ. 4
 
where each element in image F(i) is the element-wise product of corresponding elements in images W(i) and E(i). Note, W(i) is a grayscale image while E(i) is a color value such that EQ. 4 yields a color correction image F(i). With correction images F ( )  known, image A  105  may be updated (block  235 ). More specifically:
 
 A′=A−F ( i ), where  i= 1 to k.  EQ. 5
 
As above, A′ results from an element-wise operation on corresponding elements in images A  105  and F(i)
 
     One result of actions in accordance with block  135  is to make colors in image A  105  nearer to the colors in image B 2   130  based on the correspondences of all pixels across the image that have similar colors. In a simplified implementation with only 1 cluster, the result in accordance with this disclosure would be equivalent to adjusting the mean color of image A  105  to match the mean color of image B 2   130 . While the precise number of color clusters may depend on the specific embodiment&#39;s goals and operating environment, it has been found that the number of color clusters depends on the input image and the smoothness of the weighting function. In practice, for global color transformations it has been found that using between 50 and 250 clusters can be sufficient to model non-linear color and intensity errors in different parts of the visible spectrum. If spatially local color corrections are to be accounted for (e.g., red gets pinker on one side of an image but browner on the other), a larger number of color clusters may be useful (e.g., 1,000). In general, it has been found that approximately 10 pixels per cluster are required to give a stable average. In the case of very small images, this may become a limiting factor. For example, in a 30×40 or 1,200 pixel image, using more than 120 clusters may start to become unstable. In addition, if too many clusters are employed (e.g., approaching the number of pixels in image A  105  and image B 2   130 ) computational resources can become excessive. In addition, there would be no regularizing effect to mitigate image noise (a quantity in all real implementations). Part of the strength of the approached described herein is that there are typically many pixels of similar colors in the image such that the correction procedure is very stable. 
     By way of example,  FIGS. 3A-3C  illustrate one embodiment of color-refinement operations in accordance with this disclosure. In this example, image A is adopted as the reference image and image B is the “other” input image. Image B 2  represents image B after it is warped (registered) to image A using the currently estimated disparity map. In  FIG. 3A  (“Iteration  1 ”), the raw disparity is shown with no smoothing (dark regions represent “close” objects and light areas represent “distant” objects). As shown, the amount of color correction decreases from iteration  1  ( FIG. 3A ) to iteration  2  ( FIG. 3B ), to iteration  5  ( FIG. 3C ) where the amount of color correction is very small. 
     By way of another example, color correction and spatial alignment of scan line images in accordance with this disclosure is shown in  FIGS. 4A-4F . To begin, input images A and B and their corresponding input scan line images may be obtained ( FIG. 4A ). Image A will again be taken as the reference image and color correction will again be applied to non-reference image B. Using input images A and B, an initial disparity estimate may be determined ( FIG. 4B ) and used to perform an initial registration of image B with image A to generate image B 2  ( FIG. 4C ). An estimated color shift may then be determined ( FIG. 4D ) and applied to original image B to generate interim color-adjusted image Bc ( FIG. 4E ). An improved disparity estimate may be made ( FIG. 4F ), where after the entire process may be repeated per blocks  115 - 135  until a suitable termination condition is met as discussed above with respect to block  145 . 
     Referring to  FIG. 5 , the disclosed color-refinement and disparity estimation operations may be performed by representative computer system  500  (e.g., a general purpose computer system such as a desktop, laptop, notebook or tablet computer system). Computer system  500  may include one or more processors  505 , memory  510  ( 510 A and  510 B), one or more storage devices  515 , graphics hardware  520 , device sensors  525  (e.g., 3D depth sensor, proximity sensor, ambient light sensor, accelerometer and/or gyroscope), communication circuitry  530 , user interface adapter  535  and display adapter  540 —all of which may be coupled via system bus or backplane  545  which may be comprised of one or more continuous (as shown) or discontinuous communication links. Memory  510  may include one or more types of media (typically solid-state) used by processor  505  and graphics hardware  520 . For example, memory  510  may include memory cache, read-only memory (ROM), and/or random access memory (RAM). Storage  515  may include one more non-transitory storage mediums including, for example, magnetic disks (fixed, floppy, and removable) and tape, optical media such as CD-ROMs and digital video disks (DVDs), and semiconductor memory devices such as Electrically Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). Memory  510  and storage  515  may be used to retain media (e.g., audio, image, and video files), preference information, device profile information, computer program instructions or code organized into one or more modules and written in any desired computer programming language, and any other suitable data. When executed by processor(s)  505  and/or graphics hardware  520  such computer program code may implement one or more of the methods described herein. Communication circuitry  530  may be used to connect computer system  500  to one or more other networks. Illustrative networks include, but are not limited to, a local network such as a USB network, an organization&#39;s local area network, and a wide area network such as the Internet. Communication circuitry  530  may use any suitable technology (e.g., wired or wireless) and protocol (e.g., Transmission Control Protocol (TCP), Internet Protocol (IP), User Datagram Protocol (UDP), Internet Control Message Protocol (ICMP), Hypertext Transfer Protocol (HTTP), Post Office Protocol (POP), File Transfer Protocol (FTP), and Internet Message Access Protocol (IMAP)). User interface adapter  535  may be used to connect keyboard  550 , microphone  555 , pointer device  560 , speaker  565  and other user interface devices such as a touch-pad and/or a touch screen (not shown). Display adapter  540  may be used to connect one or more display units  570  which may provide touch input capability. Processor  505  may be a system-on-chip such as those found in mobile devices and include one or more dedicated graphics processing units (GPUs). Processor  505  may be based on reduced instruction-set computer (RISC) or complex instruction-set computer (CISC) architectures or any other suitable architecture and may include one or more processing cores. Graphics hardware  520  may be special-purpose computational hardware for processing graphics and/or assisting processor  505  with performing computational tasks. In one embodiment, graphics hardware  520  may include one or more programmable GPUs and each such unit may include one or more processing cores. 
     Referring to  FIG. 6 , the disclosed color-refinement and disparity estimation operations may also be performed by illustrative mobile electronic device  600  (e.g., a mobile telephone, personal media device, or a computer system in accordance with  FIG. 5 ). As shown, electronic device  600  may include one or more processors  605 , display  610 , user interface  615 , graphics hardware  620 , microphone  625 , audio codec(s)  630 , speaker(s)  635 , communications circuitry  640 , device sensors  645 , memory  650 , storage  655 , image capture circuitry or unit  660 , video codec(s)  665 , and communications bus  670 . Processor  605 , display  610 , user interface  615 , graphics hardware  620 , communications circuitry  640 , device sensors  645 , memory  650 , storage  655 , and communications bus  670  may be of the same or similar type and serve the same or similar function as the similarly named component described above with respect to  FIG. 5 . Audio signals obtained via microphone  625  may be, at least partially, processed by audio codec(s)  630 . Data so captured may be stored in memory  650  and/or storage  655  and/or output through speakers  635 . Image capture circuitry  660  may include two (or more) lens assemblies  660 A and  660 B, where each lens assembly may have a separate focal length. For example, lens assembly  660 A may have a short focal length relative to the focal length of lens assembly  660 B. Each lens assembly may have a separate associated sensor element  660 C. Alternatively, two or more lens assemblies may share a common sensor element. Image capture circuitry  660  may capture still and/or video images. Output from image capture circuitry  660  may be processed, at least in part, by video codec(s)  665  and/or processor  605  and/or graphics hardware  620 , and/or a dedicated image processing unit or pipeline incorporated within image capture circuitry  660 . Images so captured may be stored in memory  650  and/or storage  655 . 
     It is to be understood that the above description is intended to be illustrative, and not restrictive. The material has been presented to enable any person skilled in the art to make and use the disclosed subject matter as claimed and is provided in the context of particular embodiments, variations of which will be readily apparent to those skilled in the art (e.g., some of the disclosed embodiments may be used in combination with each other). For example, one variation in accordance with this disclosure would be to use, at the final step(s), a limited number (e.g., ‘p’) of the nearest color clusters for each pixel in output image A′ may be used instead of all k clusters. To aid this process, some data structure (e.g., a K-d tree) may be used to preselect only nearby pixels/clusters to actually calculate the distances for each pixel, as these computations may be expensive. Another embodiment may use a color space other than RGB. For example, any representation of color and intensity may be considered (e.g., HSV, YUV, Lab, or even a mapping to a higher dimensional space). The same procedure may also be applied to monochrome images to correct exposure deviations, although it is expected to perform better in higher dimensions due to greater diversity of the samples. In yet another embodiment, the image to be corrected may be a transformed version of the input images. For example, the disparity estimation procedure may be performed at a down-sampled resolution for speed. Color correction operation  135  may use the differences between resized versions of images A and B to obtain a new color-adjusted image which may, in the end, be re-adjusted to provide a full resolution output or final image. In still another embodiment, operation  100  may be applied to any two input images of the same scene captured at approximately the same time, by the same or different cameras, where there is some exposure difference between the images (e.g., images captured during an HDR bracket operation). To increase the operational speed of operation  100  (at the cost of some approximation), once the color correction clusters have been found, rather than calculating weights, distances, and correction images for every pixel in the output image, the color transformations may be calculated using the same procedure as described, but for a representative set of color values. This representative set of transformations may be used as a look-up table for pixels in the image to be corrected. 
     In addition to color matching, other characteristic differences between images, such as blur, could be matched and refined in a similar way to improve disparity estimation. As described herein, color adjustment operations in accordance with block  135  were designed to deal with and correct for color distortions that are uniform (or nearly so) across the images. That is to say, color (R1, G1, B1) in image X is mapped to color (R2, G2, B2) wherever it occurs spatially. An extension that could handle some spatially local color adjustments (for example in images where tone mapping, non-uniform lens vignetting, lens flare, or other local effects had occurred) may be achieved by increasing the number of clusters used (see discussion above) and sampling them locally within the image, and modifying the weighting function used in operation  135  to take account of the spatial distance between a pixel in image X and the mean spatial location of each color cluster. One approach to achieve this would be to append the spatial coordinates as additional dimensions of the vectors C A  and C B2 . In one embodiment, for example, normalized coordinates for the ‘x’ and ‘y’ positions in the image may be adopted (e.g., between 0 and 1). Then, when using the downsized A′ and/or B 2 ′, a co-ordinate of (0.5, 0.5) would always correspond to the middle of an image. These normalized co-ordinates may be appended to the input images as extra “color” components/channels. For example a yellow pixel at the middle left of the image A′ would have a 5 dimensional value (R, G, B, x, y)=(1.0, 1.0, 0.0, 0.0, 0.5). Because the images used to find the clusters are registered (see discussion above), the corresponding pixel in the image B 2 ′ might have different color values but will have the same spatial co-ordinates, e.g., (R, G, B, x, y)=(0.9, 0.9, 0.1, 0.0, 0.5). When clusters means C A′ (i) and C B2′ (i) are found, because the corresponding pixels in each image are used for each cluster, the mean values of the (x, y) part of each corresponding cluster will also have the same values, e.g., C A′ (1)=(0.7, 1.0, 0.0, 0.2, 0.3) and C B2′ (1)=(0.8, 1.0, 0.4, 0.2, 0.3). Once this, or a similar, approach has been implemented, the rest of operation  100  is exactly as described above except that only the (R, G, B) portion of the E ( )  and F ( )  vectors are meaningful (the ‘x’ and ‘y’ portions may be ignored and/or are 0). Further, silhouette processing may be applied during, or separately from, blocks  205  and  215  to determine how well each pixel belongs to its assigned cluster during generation of image I B . Further, while  FIGS. 1 and 2  show flowcharts illustrating various operations in accordance with the disclosed embodiments, in one or more embodiments, one or more of the disclosed steps may be omitted, repeated, and/or performed in a different order than that described herein. Accordingly, the specific arrangement of steps or actions shown in  FIGS. 1 and 2  should not be construed as limiting the scope of the disclosed subject matter. The scope of the invention therefore should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.”