Patent Publication Number: US-7916940-B2

Title: Processing of mosaic digital images

Description:
BACKGROUND 
     A typical digital camera includes an array of photosensors, with each photosensor sensitive to only a single color of light. For example, each photosensor is sensitive to one of red, green and blue light. During image acquisition, an image is focused on the photosensor array, and each photosensor measures or “samples” a single color of the image. If red-sensitive, green-sensitive and blue-sensitive photosensors can be located at each pixel, the photosensor array can acquire an image having “full color” at each pixel. 
     The photosensor arrays of certain digital cameras have only a single photosensor at each pixel location, and produce digital images having only a single color sample at each pixel. For example, a digital camera produces a digital image having one of red, green and blue sampled information at each pixel. Information about the other two colors at each pixel is missing. This undersampled digital image is referred to as a “mosaic” image. 
     A demosaicing algorithm may be used to transform an undersampled digital image into a digital image having full color information at each pixel value. A typical demosaicing algorithm interpolates the missing pixel information from the sampled pixel values in the mosaic image. A goal of demosaicing is reconstruction of an image that is perceived to be similar to the image focused on the photosensor array. 
     Edges and other abrupt photometric transitions present a particular problem to demosaicing. Simple demosaicing algorithms such as bilinear interpolation do not account for edges; consequently, color information at edges in the demosaiced image can be distorted (e.g., edges can be blurred). 
     More complex demosaicing algorithms try to account for edges. However, even the more complex demosaicing algorithms can introduce artifacts into demosaiced images. Zippering and fringing are typical artifacts at edges in demosaiced images. These artifacts can degrade image quality. 
     A typical digital camera can perform on-board demosaicing. This color at each pixel. However, memory and processing power of a typical digital camera are limited. The limited memory and processing power can constrain the complexity of the demosaicing algorithm and hamper the ability to account for edges and reduce artifacts at the edges during demosaicing. 
     A demosaicing algorithm that is simple and fast, and that reduces edge artifacts or blurring is desirable. Such a demosaicing algorithm is especially desirable for digital cameras. 
     SUMMARY 
     According to one aspect of the present invention, a full color digital image is produced from a mosaic digital image having sampled values. A full color output image is estimated from the mosaic image, the output image is transformed from an original color space into luminance/chrominance color space chrominance components of the transformed output image are smoothed, the image with the smoothed chrominance components is re-transformed back to the original color space, and corresponding pixels in the re-transformed image are re-set to the sampled values. 
     Other aspects and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an illustration of a photosensor arrangement for a typical digital camera, and corresponding pixels of a mosaic image. 
         FIG. 2  is an illustration of a method of performing image processing in accordance with an embodiment of the present invention. 
         FIG. 3  is an illustration of a method of performing image demosaicing in accordance with a first embodiment of the present invention. 
         FIG. 4  is an illustration of a method of performing image demosaicing in accordance with a second embodiment of the present invention. 
         FIG. 5  is an illustration of a method of performing image demosaicing in accordance with a third embodiment of the present invention. 
         FIG. 6  is an illustration of a digital imaging system in accordance with an embodiment of the present invention. 
         FIG. 7  is an illustration of an apparatus for generating an image demosaicing article in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     As shown in the drawings for purposes of illustration, the present invention is embodied in the processing of a mosaic image to produce a digital image having full color information at each pixel. One color band is inferred from other color bands. The processing is based on a Bayesian approach which exploits color band dependencies in color images. It is assumed that spatial discontinuities in different color bands are aligned and that this characteristic can be exposed in the statistics of the spatial derivatives. This allows a reduction of aliasing artifacts and enables reconstruction of frequencies that are above Nyquist. 
     Statistical dependencies between color bands may be characterized in a joint spatial-spectral (color) domain. Define a color image as a three dimensional vector field h(x; y)=(R(x; y);G(x; y);B(x; y) T , where R, G and B represent the Red, Green and Blue color bands of the image. The mutual dependencies between color values in the image can be analyzed by studying their joint distribution. The problem is that the entire distribution is defined over a huge dimensional space (3n 2  dimensional space for n×n image). This problem may be solved by approximating the joint distribution over a sub-space (low dimensional) where relevant information is still preserved. 
     Since the processing involves the prediction of one color band given another, the spatial-spectral subspace which maximizes the correlations between different color bands will is of interest. Canonical Correlation Analysis (CCA) may be used to determine the best subspace in the spatial-spectral domain. Using CCA, it was found that Spatial derivatives are the projections in which color correlations are maximized. The characteristic distribution of 3D derivative histograms (e.g., R- directional- derivative vs. G-directional-derivative vs. B-directional-derivative) suggests that directional derivatives of color images are highly similar, i.e. highly probable to be located about the main diagonal (1; 1; 1) of the 3D derivative histogram. 
     Investigating the 3D derivative histogram of natural images, the following two findings were made. First, the spatial derivatives of different color bands, taken at the same image location, are highly probable to be similar to each other (this is not necessarily true for the color values themselves). Second, the derivatives tend to be statistically independent if the image data is represented in a coordinate system which is aligned with the main diagonal. 
     Define a color space (I, c 1 , c 2 ) as 
                 (         l             c   1               c   2           )     =       T   ⁡     (         R           G           B         )       ⁢           ⁢   where       ⁢                       T   =       (           1   /     3           0       0           0         1   /     2           0           0       0         1   /     6             )     ⁢           ⁢     (         1       1       1           1         -   1         0           1       1         -   2           )             
where l represents pixel luminance and where c 1 , c 2  represent the pixel chrominance. The luminance is aligned with the main diagonal, and the two chrominance axes span the plane perpendicular to the main diagonal. In this coordinate system, the spatial derivatives (Ix; c 1 x; c 2 x) are, at a good approximation, statistically independent. Additionally, in this representation is can be observed that high spatial derivatives is highly probable to occur in the luminance band but improbable in the chrominance band.
 
     This finding is in accord with the perceptual properties of the human visual system (HVS) where the HVS is less sensitive to high frequencies in the chrominance domain compared to its sensitivity in the luminance domain. Consequently, the processing according to the present invention not only exploits color redundancies, but also minimizes the perceived difference between the actual and the reconstructed images. 
     The processing is not limited to any particular photosensor arrangement. For the purposes of illustration, the processing will be described in connection with a photosensor arrangement known as a Bayer color filter array (CFA). 
     Reference is made to  FIG. 1 , which illustrates a photosensor array  110  having photosensors  112  arranged in a Bayer CFA. The photosensors  112  are arranged in 2×2 cells  114 . Each cell  114  consists of two photosensors sensitive to green (G) light only, one photosensor sensitive to red (R) light only, and one photosensor sensitive to blue (B) light only. The cells  114  are repeated (tiled) across the photosensor array  110 . 
       FIG. 1  also illustrates a mosaic digital image  150 . Each block  154  of the mosaic image  150  corresponds to a cell  114  of the photosensor array  110 . Each pixel  152  of the mosaic image  150  is described by an n-bit word, and each n-bit word provides one of red, green and blue color sampled information. In each 2×2 block  154  of the mosaic image  150 , green information is provided at two pixels, red information is provided at one pixel, and blue information is provided at one pixel. 
     Reference is made to  FIG. 2 , which illustrates a method of processing a mosaic digital image. Pixel values of the mosaic image  150  may be denoted as m(i,j), where i and j are indices to the rows and columns of the mosaic image  150 . 
     A full color output image is estimated from the mosaic image ( 210 ). Sampled values in the mosaic image may be copied directly into their corresponding positions in the output image, and missing information in each of the green, red and blue color planes may be estimated from the sampled values. The same algorithm may be used to estimate each color plane of the output image, or different algorithms may be used. 
     The output image is transformed from RGB color space into a luminance/chrominance color space ( 212 ). The (I,c 1 ,c 2 ) color space is a particular example of a luminance/chrominance color space. Other examples of luminance/chrominance color space include without limitation YIQ, YCbCr, HSV, and CIE-LAB. 
     Chrominance components of the transformed output image are heavily smoothed ( 214 ). In terms of frequencies, light smoothing would be less aggressive in reducing high frequencies of an image, as compared to the strong smoothing. The smoothing may be isotropic or anisotropic. According to the principles achieved by the suggested statistical inference, natural images are expected to be smooth in chrominance bands, whereas high frequencies are more probable in the luminance band. Hence the luminance band may be lightly smoothed; left unchanged, or even lightly sharpened ( 214 ). 
     The output image is transformed back to its original color space ( 216 ). In this example, the output image is transformed back to RGB color space. 
     Corresponding pixel values in the re-transformed image are re-set to the original sampled values in the mosaic image ( 218 ). For example, a k×k mosaic image with a Bayer pattern has pixel inputs m(i,j), where i and j are indices to the rows and columns of the k×k mosaic image, and i,j=[0 . . . k−1]. The sampled values of the mosaic image may be copied to the output image as follows:
         I R (i,j)=m(i,j) for all red samples (i and j even),   I G (i,j)=m(i,j) for all green samples (i+j odd), and   I B (i,j)=m(i,j) for all blue samples (i and j odd),
 
where I R (i,j) represents red information at pixel (i,j) of the output image, I G (i,j) represents green information at pixel (i,j) of the output image, and I B (i,j) represents blue information at pixel (i,j) of the output image.
       

     This step imposes a constraint on the interpolation. After filtering in the luminance/chrominance color space, the filtered values are likely to deviate from the sampled (original, measured) values. Imposing the constraints by resetting to the sampled values is performed to replace output values with the sampled values in their corresponding positions. 
     Steps  212 - 218  may be repeated several times ( 220 ). These steps may converge to an optimal result. The iterations may be stopped when the result is “close enough” to the optimum. For example, the iterations can be stopped when the improvement (e.g., Euclidean distance between successive iterations) becomes smaller than a threshold. Alternatively, a fixed number of iterations can be performed. The fixed number would typically bring the output close enough to the optimum. 
     Post-processing such as sharpening may be performed on the output image after the last iteration ( 222 ). The sharpening may be performed to compensate blurring due to system optics. Conventional sharpening such as unsharp masking or Wiener filtering may be performed. The post-processing is application-specific. In a digital camera for example, the post-processing may include color transformation, tone reproduction, sharpening, de-noising and compression. 
     The sampled values may be pre-processed ( 224 ). The pre-processing is application-specific. In a digital camera, for example, pre-processing between image acquisition and demosaicing may include flare reduction, data linearization, color balance, and de-noising. De-noising may be performed if the sampled pixel values (that is, the raw data) are noisy or expected to be noisy. Noisy raw data can cause artifacts and noise to appear in the demosaiced image. 
     Reference is made to  FIG. 3 , which illustrates a first embodiment of a method of producing a full color digital image from a mosaic digital image. Sampled values in the mosaic image are copied directly into their corresponding positions in the output image ( 310 ). A linear algorithm such as bilinear or bi-cubic interpolation is used to estimate missing information in each of the green, red and blue color planes ( 312 ). The full color output image is transformed to (I,c 1 ,c 2 ) color space ( 314 ). 
     Isotropic smoothing is performed on the transformed output image ( 316 ). The smoothing includes light isotropic smoothing of the luminance component of the transformed image, and heavy isotropic smoothing of the chrominance components. The isotropic smoothing may involve low pass filtering, or a convolution with a Gaussian with a specific variance. The variance of the Gaussian controls the amount of smoothing. The isotropic smoothing is also a linear operation. 
     The output image is transformed back to its original color space ( 318 ). Corresponding pixel values in the re-transformed image are re-set to the sampled values ( 320 ). 
     Additional iterations may be performed ( 322 ). Each additional iteration includes steps  314 - 320 . The number of iterations may be a fixed number. 
     Since all steps are linear, a fixed number of iterations can be concatenated into a single linear operation. If the pre-processing and post-processing operations are also linear, those operations may also be concatenated into the single linear operation. 
     From this single linear operation, a set of linear convolution kernels can be generated. The single linear operation can be treated as block shift-invariant filter, which allows the filter kernels to be generated by using the method disclosed in assignee&#39;s U.S. Ser. No. 10/638,755 filed Aug. 9, 2003. For a Bayer CFA, there would be twelve kernels: four kernels per cell for each color plane. 
     Reference is made to  FIG. 4 , which illustrates a second embodiment of a method of producing a full color digital image from a mosaic digital image. Sampled values in the mosaic image are copied directly into their corresponding positions in the output image ( 410 ). An algorithm is used to estimate missing information in each of the green, red and blue color planes ( 412 ). The algorithm may be linear (e.g., bilinear interpolation) or non-linear. Exemplary non-linear interpolation algorithms are disclosed in U.S. Pat. No. 6,404,918 (directional smoothing), U.S. Patent Application 20020186309 published Dec. 12, 2002 (bilateral filtering), and an algorithm disclosed in assignee&#39;s U.S. Ser. No. 10/743,623 filed Dec. 22, 2003. 
     The full color output image is transformed to (I,c 1 ,c 2 ) color space ( 414 ), and smoothing is performed on the transformed output image ( 416 ). The smoothing includes heavy isotropic smoothing of the chrominance components. 
     Smoothing may also be performed on the luminance component. The smoothing may include directional or anisotropic smoothing in the luminance component. Anisotropic smoothing is context dependent, such that pixel values appearing across an edge are lightly weighted in the estimation. An advantage of such smoothing is that edges are preserved. In the alternative, no smoothing or sharpening of the luminance component may be performed. 
     The output image is transformed back to its original color space ( 418 ), and corresponding pixel values in the re-transformed image are re-set to the sampled values in the mosaic image ( 420 ). 
     Additional iterations may be performed ( 422 ). Each additional iteration includes steps  414 - 420 . If any of these steps are non-linear, the iterations cannot be concatenated into a single batch operation. 
     Reference is made to  FIG. 5 , which illustrates a third embodiment of a method of producing a full color digital image from a mosaic digital image. A first one of the color planes is fully populated ( 510 ). That is, missing information in the first plane is estimated. The first color plane may be fully populated by a linear or non-linear algorithm. As a first example, the first color plane may be fully populated by bilinear interpolation. As a second example, the first plane may be fully populated by a directional smoothing method disclosed in U.S. Pat. No. 6,404,918. As a third example, the first color plane may be fully populated by a bilateral filtering method disclosed in U.S. Patent Application 20020186309 published Dec. 12, 2002. As a fourth example, the first color plane may be fully populated by an algorithm disclosed in U.S. Ser. No. 10/743,623. 
     Other algorithms may be used as well to populate the first color plane. Exemplary anisotropic interpolation techniques are disclosed in M. Black and A. Rangarajan, “On the unification of line processes, outlier rejection, and robust statistics with applications in early vision,” International Journal of Computer Vision 19 (1996), no. 1, 57-92; P. Perona and J. Malik, “Scale space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990); and C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” IEEE International Conference on Computer Vision &#39;98 (New Delhi, India), 1998. 
     The remaining color planes are estimated ( 512 ). The algorithm used to estimate missing information in the remaining color planes may be different than the algorithm used in step  510 . 
     The output image is transformed to luminance/chrominance color space ( 514 ), the transformed image is smoothed ( 516 ), and the smoothed image is transformed back to the original color space ( 518 ). 
     Pixel values of the first color plane of the output image are re-set to their corresponding sampled values ( 520 ) or to their values estimated at step  510 . Pixel values in the remaining color planes of the re-transformed image are re-set to their corresponding sampled values ( 520 ). 
     Additional iterations may be performed ( 522 ). Each additional iteration may include steps  514 - 520 . 
     In a mosaic image that corresponds to a Bayer CFA, the green color plane is more densely sampled than the red and blue color planes. In the method of  FIG. 5  it is preferable to first interpolate the more densely sampled (green) color plane at step  510 , and estimate the remaining (red and blue) color planes at step  512 . At step  520  the green plane of the output image is reset to the sampled values and those green values estimated at step  510 , and the red and blue planes of the output image are re-set to sampled red and blue values. If linear smoothing is performed at step  516 , the entire estimation of the red and blue color planes can be applied in a single batch operation, even if the green color interpolation is performed with a non-linear algorithm. 
     Reference is made to  FIG. 6 , which illustrates a digital imaging system  610 . The system  610  includes a photosensor array  612  such as a CCD. The photosensor array  612  produces mosaic digital images. Photosensors of the array  612  may be arranged in a Bayer CFA. 
     The digital imaging system  610  also includes an image processor  614 . The image processor  614  includes a processing unit  616  and memory  618  (e.g., EPROM). Data  620  encoded in the memory  618  can be used by the processing unit  616  to transform mosaic images into digital images having full color information at each pixel in accordance with the present invention. 
     The photosensor array  612  and the image processor  614  may be packaged separately. For example, the photosensor array  612  may be part of a capture device (e.g., a scanner, a digital camera), and the image processor  614  may be implemented in a personal computer or workstation. The processing unit  616  may be a general purpose processor, and the data  620  may include a demosaicing program, which instructs the general purpose processor to transform mosaic images into digital images having full color information in accordance with the present invention. Mosaic images may be supplied to the image processor  614  directly by the capture device or indirectly (via the Internet, accessed from remote or local storage, etc.). 
     The photosensor array  612  and the image processor  614  may be contained in a single package. For example, the image processing system  610  may be a digital camera. 
     As a first example, the processing unit  616  of the digital camera may be a digital signal processor, and the data  620  may be an image processing program. The image processing program instructs the digital signal processor to transform mosaic images into digital images having full color information in accordance with the present invention. 
     As a second example, the processing unit  616  of the digital camera may include an application-specific integrated circuit (ASIC), and the data  620  may include filter kernels. The ASIC convolves the filter kernels with mosaic images to produce digital images having full color information at each pixel. The filter kernels are used to perform linear demosaicing in accordance with the present invention. 
     Full color images generated by the system  610  can be distributed in any number of ways. For example, the full color images can be distributed via a removable medium  622  such as an optical disc (e.g., DVD) or transmitted (e.g., over the Internet) from memory of one machine to another. 
     Reference is now made to  FIG. 7 , which illustrates a machine  710  for generating a demosaicing program or filter kernels. The machine  710  includes a general purpose processor  712  and memory  714  for instructing the processor  712  to generate the demosaicing program or the filter kernels. The machine  710  may be, for example, a personal computer or workstation. 
     The machine  710  may generate the demosaicing program by compiling source code into executable code. The executable code may be distributed in variety of ways. For example, the executable code may be distributed to a digital camera by burning the code into memory of the digital camera. The executable code can be distributed (e.g., as an installable package) to a computer via a network transmission, removable storage medium (e.g., optical disc)  718 , etc. 
     The machine  710  may execute a program that generates the filter kernels. The filter kernels may be distributed, for example, via the removable medium  718 , by burning the kernels into memory of a digital camera, etc. 
     The present invention is not limited to the specific embodiments described above. Instead, the present invention is construed according to the following claims.