Patent Abstract:
A method of reducing noise in a color filter array encoded image, comprising using an image sensor having a color filter array to capture an image of a scene and using such capture image to provide a color filter array encoded image; low pass filtering the color filter array encoded image to provide a base image and using such base image and the color filter array encoded image to provide a residual image; processing the base image to provide a full color fully processed base image; converting the residual image using color filter array interpolation to provide a full color residual image; and combining the full color fully processed base image and the full color residual image to provide a noised reduced fully processed image.

Full Description:
CROSS REFERENCE TO RELATED APPLICATION  
       [0001]     Reference is made to commonly-assigned U.S. patent application Ser. No. 09/742,957 filed Dec. 20, 2000, entitled “A Multiresolution Based Method for Removing Noise from Digital Images” by Gindele, the disclosure of which is incorporated herein.  
       FIELD OF THE INVENTION  
       [0002]     The invention relates generally to the field of digital image processing, and in particular to a method of noise-reducing digital camera images.  
       BACKGROUND OF THE INVENTION  
       [0003]     An essential step in most every digital camera image processing chain is noise-reduction. The prior art is rich with methods for accomplishing noise-reduction. Many, if not most, treat the problem generically as a task of noise-reduction of digital images, regardless of the source of said digital images. In this regard, it is not germane that the image was initially created by a digital camera. One class of inventions in this category uses a multiresolution (multi-scale) decomposition of the digital image, usually in the form of a Laplacian pyramid. This representation of the digital image generally permits effective noise-reduction of the digital image, both in terms of resulting image quality and required computational effort. For this reason, multiresolution noise-reduction methods are popular and many implementations exist. U.S. Pat. No. 5,488,374 (Frankot, et al.) teaches using a multi-scale adaptive filter for smoothing interferometric SAR data. U.S. Pat. No. 5,526,446 (Adelson, et al.) discloses converting an image into a set of coefficients in a multi-scale image decomposition process, and modifying the coefficients in order to reduce noise. U.S. Pat. No. 5,729,631 (Wober, et al.) reveals representing an image in a pyramid structure, using a Wiener variant filter to reduce noise in each pyramid level. U.S. Pat. No. 5,963,676 (Wu, et al.) describes a multi-scale approach that reduces noise using directional sensitive Laplacian kernels at each pyramid level. Above-cited commonly assigned U.S. patent application Ser. No. 09/742,597 (U.S. Patent Application Publication 2002/0118887) full color fully processed base image teaches decomposing an image into a multiresolution representation and noise-reducing in different ways the base and residual image components of the representation to achieve a noise-reduced final image.  
         [0004]     All of these examples assume the initial image to be noise-reduced is in the same fundamental data representation as the final image to be produced by the subsequent full image processing chain, e.g., a full-color, three-channel RGB. In digital cameras, however, the initial image as produced by the camera sensor is usually a grayscale image into which color data is encoded with the use of a color filter array. The final image of the full image processing chain is a full-color, three-channel RGB image. The examples of multiresolution noise-reduction cited in the previous paragraph do not address the transformations required to accept a color filter array-encoded grayscale image upon input and to produce a noise-reduced full-color image upon output, particularly, as in the case of Gindele, when the base and residual image components go through different image processing paths. Noise-reduction theory indicates that the sooner in a sequence of image processing steps that the noise component can be removed from the image, the better will be the chances of performing effective noise-reduction without significant degradation of the genuine signal content of the image.  
         [0005]     What is needed is a method of noise-reducing a digital camera image that accepts a color filter array-encoded grayscale image on input and produces a full-color fully processed image on output that permits a multiresolution decomposition of the image to be noise-reduced and separate processing paths for the base and residual image components. Any proposed solution should be independent of the nature of the color filter array encoding scheme used and the subsequent image processing steps used to fully process the image.  
       SUMMARY OF THE INVENTION  
       [0006]     It is the object of the present invention to provide an effective way to noise-reduce a digital camera image that accepts a color filter array-encoded grayscale image on input and produces a full-color fully processed image on output.  
         [0007]     It is another object to permit a multiresolution decomposition of the image to be noise-reduced using separate processing paths for the base and residual image components.  
         [0008]     This object is achieved by a method of reducing noise in a color filter array encoded image, comprising: 
        (a) using an image sensor having a color filter array to capture an image of a scene and using such capture image to provide a color filter array encoded image;     (b) low pass filtering the color filter array encoded image to provide a base image and using such base image and the color filter array encoded image to provide a residual image;     (c) processing the base image to provide a full color fully processed base image;     (d) converting the residual image using color filter array interpolation to provide a full color residual image; and     (e) combining the full color fully processed base image and the full color residual image to provide a noised reduced fully processed image.        
 
         [0014]     It is an advantage of the present invention that it is independent of the nature of the color filter array encoding scheme used.  
         [0015]     It is another advantage of the present invention that the noise-reduction ability is maximized by operating on the image at the earliest possible position within the image processing chain. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0016]      FIG. 1  is a perspective diagram of a computer system for implementing the present invention;  
         [0017]      FIG. 2  is a diagram of the full image processing chain of the preferred embodiment of this invention;  
         [0018]      FIG. 3  is a diagram of four example color filter array encoding schemes;  
         [0019]      FIG. 4  is a diagram of the low pass filter operation;  
         [0020]      FIG. 5  is a diagram of the Laplacian pyramid decomposition step;  
         [0021]      FIG. 6  is a diagram of the Laplacian pyramid level decomposition step;  
         [0022]      FIG. 7  is a diagram of the result of the downsample image operation;  
         [0023]      FIG. 8  is a diagram of the cubic interpolation neighborhood used by the upsample image step;  
         [0024]      FIG. 9A  is a diagram of the noise-reduction of image components step for a base image component;  
         [0025]      FIG. 9B  is a diagram of the noise-reduction of image components step for a residual image component;  
         [0026]      FIG. 10  is a diagram of the neighborhood used by the adaptive median filter;  
         [0027]      FIG. 11  is a diagram of an neighborhood used by the standard sigma filter;  
         [0028]      FIG. 12  is a diagram of the Laplacian pyramid recomposition step;  
         [0029]      FIG. 13  is a diagram of the full image processing chain for an alternate embodiment of this invention;  
         [0030]      FIG. 14  is a diagram of the full image processing chain for an alternate embodiment of this invention;  
         [0031]      FIG. 15  is a diagram of the full image processing chain for an alternate embodiment of this invention;  
         [0032]      FIG. 16  is a diagram of the full image processing chain for an alternate embodiment of this invention; and  
         [0033]      FIG. 17  is a diagram of the full image processing chain for another alternate embodiment of this invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0034]     In the following description, a preferred embodiment of the present invention will be described in terms that would ordinarily be implemented as a software program. Those skilled in the art will readily recognize that the equivalent of such software can also be constructed in hardware. Because image manipulation arrangements and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, the system and method in accordance with the present invention. Other aspects of such algorithms and systems, and hardware and/or software for producing and otherwise processing the image signals involved therewith, not specifically shown or described herein, can be selected from such systems, algorithms, components and elements known in the art. Given the system as described according to the invention in the following materials, software not specifically shown, suggested or described herein that is useful for implementation of the invention is conventional and within the ordinary skill in such arts.  
         [0035]     Still further, as used herein, the computer program can be stored in a computer readable storage medium, which can include, for example; magnetic storage media such as a magnetic disk (such as a hard drive or a floppy disk) or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; solid state electronic storage devices such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program.  
         [0036]     Before describing the present invention, it facilitates understanding to note that the present invention is preferably utilized on any well-known computer system, such as a personal computer. Consequently, the computer system will not be discussed in detail herein. It is also instructive to note that the images are either directly input into the computer system (for example by a digital camera) or digitized before input into the computer system (for example by scanning an original, such as a silver halide film).  
         [0037]     Referring to  FIG. 1 , there is illustrated a computer system  110  for implementing the present invention. Although the computer system  110  is shown for the purpose of illustrating a preferred embodiment, the present invention is not limited to the computer system  110  shown, but can be used on any electronic processing system such as found in home computers, kiosks, retail or wholesale photofinishing, or any other system for the processing of digital images. The computer system  110  includes a microprocessor-based unit  112  for receiving and processing software programs and for performing other processing functions. A display  114  is electrically connected to the microprocessor-based unit  112  for displaying user-related information associated with the software, e.g., by means of a graphical user interface. A keyboard  116  is also connected to the microprocessor based unit  112  for permitting a user to input information to the software. As an alternative to using the keyboard  116  for input, a mouse  118  can be used for moving a selector  120  on the display  114  and for selecting an item on which the selector  120  overlays, as is well known in the art.  
         [0038]     A compact disk-read only memory (CD-ROM)  124 , which typically includes software programs, is inserted into the microprocessor-based unit  112  for providing a means of inputting the software programs and other information to the microprocessor based unit  112 . In addition, a floppy disk  126  can also include a software program, and is inserted into the microprocessor-based unit  112  for inputting the software program. The compact disk-read only memory (CD-ROM)  124  or the floppy disk  126  can alternatively be inserted into an externally located disk drive unit  122  which is connected to the microprocessor-based unit  112 . Still further, the microprocessor-based unit  112  can be programmed, as is well known in the art, for storing the software program internally. The microprocessor-based unit  112  can also have a network connection  127 , such as a telephone line, to an external network, such as a local area network or the Internet. A printer  128  can also be connected to the microprocessor-based unit  112  for printing a hardcopy of the output from the computer system  110 .  
         [0039]     Images can also be displayed on the display  114  via a personal computer card (PC card)  130 , such as, as it was formerly known, a PCMCIA card (based on the specifications of the Personal Computer Memory Card International Association) which contains digitized images electronically embodied in the PC card  130 . The PC card  130  is ultimately inserted into the microprocessor-based unit  112  for permitting visual display of the image on the display  114 . Alternatively, the PC card  130  can be inserted into an externally located PC card reader  132  connected to the microprocessor-based unit  112 . Images can also be input via the compact disk  124 , the floppy disk  126 , or the network connection  127 . Any images stored in the PC card  130 , the floppy disk  126  or the compact disk  124 , or input through the network connection  127 , can have been obtained from a variety of sources, such as a digital camera  134  or a scanner (not shown). Images can also be input directly from the digital camera  134  via a camera docking port  136  connected to the microprocessor-based unit  112  or directly from the digital camera  134  via a cable connection  138  to the microprocessor-based unit  112  or via a wireless connection  140  to the microprocessor-based unit  112 .  
         [0040]     In accordance with the invention, the programs embodying the present invention can be stored in any of the storage devices heretofore mentioned and applied to images in order to construct a noise-reduced image.  
         [0041]      FIG. 2  is a block diagram of the image processing chain that is the preferred embodiment of this invention. The color filter array encoded image  10  is acquired by the digital camera  134 ,  FIG. 1  and provides selected pixel patterns. A copy of the color filter array encoded image  10  is low pass filtered by low pass filter operation  12 . The result of the low pass filter operation  12  is the base image  18  of this embodiment. The base image  18  is subtracted from a copy of the original color filter array encoded image  10  in the image difference operation step  14 . The result of the image difference operation is the residual image  16  of this embodiment. The residual image  16  is still in a color filter array encoded representation. The color filter array interpolation step  20  creates a full color residual image  22 . The full image processing step  24  performs the standard image processing operations associated with digital camera image processing chain, such as, but not limited to, color filter array interpolation, color and tone correction, and sharpening, to the base image  18 . The result of the full image processing  24  is a fully processed base image  26 . The full color residual image  22  and the fully processed base image  26  are added together in the image summation operation  28 . The result of the image summation operation  28  is a full color fully processed image  30 . The full color fully processed image  30  is the resulting noise-reduced image created by this embodiment of this invention.  
         [0042]     The nature of the color filter array encoding is unimportant for this invention.  FIG. 3  shows four examples of color filter array encoding that are compatible with the current invention. In  FIG. 3 , encoding A represents a three-color tessellation kernel with R, G, and B denoting red, green, and blue. In encoding B, a four-color tessellation kernel is shown with R, G 1 , G 2 , and B denoting red, a first shade of green, a second shade of green, and blue. In encoding C, a four-color tessellation kernel in shown with C, M, Y, and G denoting cyan, magenta, yellow, and green. In encoding D, a three-color tessellation kernel is shown with C, M, and Y denoting cyan, magenta, and yellow. It will be evident to one skilled in the art that other color filter array encoding schemes are possible.  
         [0043]     The nature of the low pass filtering operation  12  in  FIG. 2  can be the application of a simple convolution kernel such as  
         1   16     ⁢     (         1       2       1           2       4       2           1       2       1         )         
        or can take the form of a noise cleaning operation such as a median filter and/or a sigma filter. Regardless of its nature, the low pass filtering operation  12  must perform its actions with respect to the color filter array encoding used. This usually means restricting any given computation to one specific set of colors within the encoding. Returning to the previously cited simple convolution kernel, the variation that would work for any of the encodings presented in  FIG. 3  would be  
         1   16     ⁢     (         1       0       2       0       1           0       0       0       0       0           2       0       4       0       2           0       0       0       0       0           1       0       2       0       1         )         
       
 
         [0045]     Regardless of where this convolution kernel is placed in the color filter array image, the nonzero coefficients will always correspond to the same color within the encoding. Convolving the color filter array encoded image  10  with this kernel will produce a low pass filtered version of the color filter array encoded image that still maintains the color encoding integrity of the encoding scheme. If other operations, such as median and/or sigma filters are used instead of this convolution kernel, they must be similarly modified to retain the integrity of the color encoding scheme.  
         [0046]     In the preferred embodiment, the details of the low pass filtering operation  12  are depicted in  FIG. 4 . The color filter array encoded image  10  is decomposed into a Laplacian pyramid representation by the Laplacian pyramid decomposition step  32 . The noise-reduction of image components operation  34  noise-reduces the residual image components of the Laplacian pyramid representation, as well as the lowest-resolution base image component of the Laplacian pyramid representation. The Laplacian pyramid recomposition step  36  reconstitutes a full-resolution image using the noise-reduced components produced by the noise-reduction of image components operation  34 .  
         [0047]     In  FIG. 5 , the Laplacian pyramid decomposition step  32  is presented in more detail. The color filter array encoded image  10 ,  FIG. 2 , is decomposed into a residual image component  40 A and a base image component  42 A by the Laplacian pyramid level decomposition step  38 A. Using the base image component  42 A as input, this process is repeated in blocks  38 B,  40 B, and  42 B to create second level of pyramid decomposition.  FIG. 5  explicitly shows the creation of two pairs of residual and base image component. It should be evident to one skilled in the art that this process can be repeated as many times as desired to create pyramid composition consisting of more than two levels.  
         [0048]     In  FIG. 6 , the Laplacian pyramid level decomposition step  38  is presented in more detail. The color filter array encoded image  10 ,  FIG. 2 , or a base image component  42 ,  FIG. 5 , is blurred in the blur image step  44  using the following convolution kernel:  
         1   256     ⁢     (         1       0       4       0       6       0       4       0       1           0       0       0       0       0       0       0       0       0           4       0       16       0       24       0       16       0       4           0       0       0       0       0       0       0       0       0           6       0       24       0       36       0       24       0       6           0       0       0       0       0       0       0       0       0           4       0       16       0       24       0       16       0       4           0       0       0       0       0       0       0       0       0           1       0       4       0       6       0       4       0       1         )         
 
         [0049]     The downsample image step  46  discards alternate pairs of rows and columns of the blurred image to reduce the image resolution to half of its starting resolution.  FIG. 7  shows the results of the downsample image step  46  on a 4×4 region of the color filter array encoded image  10 . Though the encoding scheme of  FIG. 3 , encoding scheme A is shown in  FIG. 7 , all of the encoding schemes shown in  FIG. 3  would be treated in the same manner. The result of the downsample image step  46  is a base image component  42 . To continue the discussion of the Laplacian pyramid level decomposition step  38 , a copy of the base image component  42  is upsampled in the upsample image step  48 . In the preferred embodiment a standard bicubic interpolation, modified to respect the color filter array encoding of the image data, is used to double the resolution of the image.  FIG. 8  is a portion of the upsampled image with blanks inserted at the locations that will be populated by the upsample image step  48 . The computation of example values R*, G*, and B* are performed with the following cubic interpolation equations: 
 
 R *=(− R 01+9 R 03+9 R 05− R 07)/16 
 
 G *=(− G 00+9 G 02+9 G 04 −G 06)/16 
 
 B *=(− B 08+9 B 10+9 B 12 −B 14)/16 
 
 By using these equations and their vertically-oriented equivalents, the entire image is upsampled in a way that respects the color filter array encoding. Though the encoding scheme A of  FIG. 3  is shown in  FIG. 8 , all of the encoding schemes shown in  FIG. 3  would be treated in the same manner. 
 
         [0050]     The results of the blur image  44  and upsample image  48  steps are subtracted from each other in the subtract images step  50 . The result of the subtract images step  50  is the residual image component  40 .  
         [0051]     Returning to  FIG. 4 , the residual image components  40  and the lowest resolution base image component  42  ( 42 B in  FIG. 5 ) are noise-reduced in the noise-reduction of image components step  34 .  FIGS. 9A and 9B  show the noise-reduction of image components step  34  in more detail. In the preferred embodiment, this noise-reduction takes the form of an adaptive median filter  52  followed by a standard sigma filter  54 . In  FIG. 9A  the lowest resolution base image component  42  ( 42 B in  FIG. 5 ) is noise-reduced to produce a noise reduced base image component  56 . In  FIG. 9B  a residual image component  40  is noise-reduced to produce a noise-reduced residual image component  5 .  FIG. 10  show a 3×3 portion of the residual image components  40  and the lowest resolution base image component  42  to be noise-reduced by the adaptive median filter  52 . Four medians are computed in four different directions within the region: 
 
h=median{G3,G4,G5}
 
v=median{G1,G4,G7}
 
s=median{G2,G4,G6}
 
b=median{G0,G4,G8}
 
 From this set of four median values, the value that is closest to the original pixel value G4 becomes the noise-reduced pixel value for G4. This process is performed for all pixel values in the image component being noise-reduced. The region used by the subsequent standard sigma filter  54  for computation is shown in  FIG. 11 . Only the values from the pixels labeled “G” are used by the sigma filter. In all other respects, the sigma filter operation is standard. The adaptive median and sigma filter operations as described here will work for all of the encoding schemes shown in  FIG. 3 . 
 
         [0052]     Returning to  FIG. 4 , the noise cleaned image components  56  and  58  are used by the Laplacian pyramid recomposition step  36  to produce the base image  18 ,  FIG. 2 .  FIG. 12  shows the Laplacian pyramid recomposition step  36  in more detail. The noise-reduced base image component  56 ,  FIG. 9A  is upsampled by a factor of two by the upsample image operation  48 A, previously described in conjunction with  FIG. 8 . The upsampled noise-reduced base image component is added to the lowest resolution noise-reduced residual image component  58 A,  FIG. 9B  (derived from residual image component  40 B,  FIG. 5 ) in the add images step  60 A. The result of the add images step  60 A is the noise-reduced base image component  62 A. It is noted that noise-reduced base image component  62 A has twice the number of rows and columns as noise-reduced base image component  56 . This process is repeated in blocks  48 B,  60 B, and  58 B to produce noise-reduced base image component  62 B. Noise-reduced base image component  62 B, in turn, become base image  18 . As in the discussion of  FIG. 5 , it should be evident to one skilled in the art that while  FIG. 12  only shows a two-level pyramid reconstruction process, the method can be directly extended to a pyramid with more levels. The only constraint is that  FIG. 5  and  FIG. 12  must work with the same number of pyramid levels.  
         [0053]     The color filter array interpolation step  20 ,  FIG. 2 , generally can be any color filter array interpolation method known to one skilled in the art. Such a method suitable for all of the color filter array encodings shown in  FIG. 3  is described in U.S. Pat. No. 6,133,953 (Okada).  
         [0054]      FIG. 13  is an alternate embodiment of this invention in which the full color residual image  22  is noise-reduced by a noise-reduced full color residual image operation  64  so as to create a noise-reduced full color image  66  prior to image summation operation  28 . The noise-reduced full color residual image operation  64  is similar to the low pass filter operation  12  except there is no longer a need to respect the color filter array encoding scheme used in creating the color filter array encoded image  10 . That being said, it is still possible to use the exact form of the low pass filter operation  12  for the noise-reduced full color residual image operation  64 , and in the case of noise-reducing an extremely noisy image it is the preferred approach. It should be evident to one skilled in the art that some or all of the color filter array encoding specific operations of the low pass filter operation  12  can be replaced with equivalent standard full-color operations in the noise-reduced full color residual image operation  64 .  
         [0055]      FIG. 14  is an alternative embodiment of this invention in which the noise-reduced full color residual image  66  is scaled in a scale full color residual image step  68  prior to the image summation operation  28 . The scale full color residual image operation  68  is a simple multiplicative scaling of all the pixel values in the noise-reduced full-color residual image  66 . Typically, a scale factor of 3.0 produces increased sharpness in the full color fully processed image  30  without significant loss of image quality due to amplification of the remaining noise in the noise-reduced full-color residual image  66 .  
         [0056]      FIG. 15  is an alternative embodiment of this invention in which the full color residual image  22  is scaled in the scale full color residual image step  68  prior to the noise-reduced full color residual image step  64 . The result is a scaled noise-reduced full color residual  74 . As described in the previous embodiment, a scale factor of 3.0 produces increased sharpness in the full color fully processed image  30  without significant loss of image quality due to amplification of the remaining noise in the noise-reduced full-color residual image  66 .  
         [0057]      FIG. 16  is an alternative embodiment of this invention, based on the alternative embodiment of  FIG. 14 , in which full color fully processed image  30  is noise-reduced by the noise-reduced fully processed image step  70  to produce an additionally noise-reduced fully processed image  72 . The details of the noise-reduced fully processed image step  70  are identical to the noise-reduced full color residual image operation  64 , including the decision to use color filter array encoding specific operations versus standard full-color operations.  
         [0058]      FIG. 17  is another alternative embodiment of this invention, based on the alternative embodiment of  FIG. 15 , in which full color fully processed image  30  is noise-reduced by the noise-reduced fully processed image step  70  to produce an additionally noise-reduced fully processed image  72 . The details of the noise-reduced fully processed image step  70  are identical to the noise-reduce full color residual image operation  64 , including the decision to use color filter array encoding specific operations versus standard full-color operations.  
         [0059]     The specific algorithms disclosed in the preferred embodiments of the present invention can be employed in a variety of user contexts and environments. Exemplary contexts and environments include, without limitation, wholesale digital photofinishing (which involves exemplary process steps or stages such as film in, digital processing, prints out), retail digital photofinishing (film in, digital processing, prints out), home printing (home scanned film or digital images, digital processing, prints out), desktop software (software that applies algorithms to digital prints to make them better—or even just to change them), digital fulfillment (digital images in—from media or over the web, digital processing, with images out—in digital form on media, digital form over the web, or printed on hard-copy prints), kiosks (digital or scanned input, digital processing, digital or scanned output), mobile devices (e.g., PDA or cell phone that can be used as a processing unit, a display unit, or a unit to give processing instructions), and as a service offered via the World Wide Web.  
         [0060]     In each case, the program to produce noise-reduced images can stand alone or can be a component of a larger system solution. Furthermore, the interfaces with the program, e.g., the scanning or input, the digital processing, the display to a user (if needed), the input of user requests or processing instructions (if needed), the output, can each be on the same or different devices and physical locations, and communication between the devices and locations can be via public or private network connections, or media based communication. Where consistent with the foregoing disclosure of the present invention, the algorithm(s) themselves can be fully automatic, can have user input (be fully or partially manual), can have user or operator review to accept/reject the result, or can be assisted by metadata (metadata that can be user supplied, supplied by a measuring device (e.g. in a camera), or determined by an algorithm). Moreover, the program(s) can interface with a variety of workflow user interface schemes.  
         [0061]     The algorithms to produce noise-reduced images disclosed herein in accordance with the invention can have interior components that utilize various data detection and reduction techniques (e.g., face detection, eye detection, skin detection, flash detection)  
         [0062]     The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention.  
       PARTS LIST  
       [0000]    
       
           10  Color Filter Array Encoded Image  
           12  Low pass Filter  
           14  Image Difference Operation  
           16  Residual Image  
           18  Base Image  
           20  Color Filter Array Interpolation  
           22  Full Color Residual Image  
           24  Full Image Processing  
           26  Fully Processed Base Image  
           28  Image Summation Operation  
           30  Full Color Fully Processed Image  
           32  Laplacian Pyramid Decomposition  
           34  Noise-reduction of Image Components  
           36  Laplacian Pyramid Recomposition  
           38  Laplacian Pyramid Level Decomposition  
           38 A Laplacian Pyramid Level Decomposition  
           38 B Laplacian Pyramid Level Decomposition  
           40  Residual Image Component  
           40 A Residual Image Component  
           40 B Residual Image Component  
           42  Base Image Component  
           42 A Base Image Component  
           42 B Base Image Component  
           44  Blur Image  
           46  Downsample Image  
           48  Upsample Image  
           48 A Upsample Image  
           48 B Upsample Image  
           50  Subtract Images  
           52  Adaptive Median Filter  
           54  Standard Sigma Filter  
           56  Noise-reduced Base Image Component  
           58  Noise-reduced Residual Image Component  
           58 A Noise-reduced Residual Image Component  
           58 B Noise-reduced Residual Image Component  
           60 A Add Images  
           60 B Add Images  
           62 A Noise-reduced Base Image Component  
           62 B Noise-reduced Base Image Component  
           64  Noise-reduced Full Color Residual Image Operation  
           66  Noise-reduced Full Color Residual Image  
           68  Scale Full Color Residual Image  
           70  Noise-reduce Fully Processed Image  
           72  Additionally Noise-reduced Fully Processed Image  
           74  Scaled Noise-reduced Full Color Residual  
           110  Computer System  
           112  Microprocessor-based Unit  
           114  Display  
           116  Keyboard  
           118  Mouse  
           120  Selector on Display  
           122  Disk Drive Unit  
           124  Compact Disk—read Only Memory (CD-ROM)  
           126  Floppy Disk  
           127  Network Connection  
           128  Printer  
           130  Personal Computer Card (PC card)  
           132  PC Card Reader  
           134  Digital Camera  
           136  Camera Docking Port  
           138  Cable Connection  
           140  Wireless Connection

Technology Classification (CPC): 6