Patent Publication Number: US-2010123009-A1

Title: High-resolution interpolation for color-imager-based optical code readers

Description:
RELATED APPLICATIONS 
     This application claims benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/116,425, titled “High-Resolution Interpolation for Color-Imager-Based Optical Code Readers,” filed Nov. 20, 2008, the entire contents of which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The field of this disclosure relates generally to imaging and more particularly, but not exclusively, to systems and methods for reading optical codes. 
     BACKGROUND INFORMATION 
     Optical codes encode useful, optically-readable information typically about the items to which they are attached or otherwise associated. Perhaps the best example of an optical code is the bar code. Bar codes are ubiquitously found on or associated with objects of various types, such as the packaging of retail, wholesale, and inventory goods; retail product presentation fixtures (e.g., shelves); goods undergoing manufacturing; personal or company assets; and documents. By encoding information, a bar code typically serves as an identifier of an object, whether the identification be to a class of objects (e.g., containers of milk) or a unique item (e.g., U.S. Pat. No. 7,201,322). A typical linear or one-dimensional bar code, such as a UPC code, consists of alternating bars (i.e., relatively dark areas) and spaces (i.e., relatively light areas). In a UPC code, for example, the pattern of alternating bars and spaces and the widths of those bars and spaces represent a string of binary ones and zeros, wherein the width of any particular bar or space is an integer multiple of a specified minimum width, which is called a “module” or “unit.” Thus, to decode the information, a bar code reader must be able to reliably discern the pattern of bars and spaces, such as by determining the locations of edges demarking adjacent bars and spaces from one another, across the entire length of the bar code. 
     Linear bar codes are just one example of the many types of optical codes in use today. Higher-dimensional optical codes, such as, two-dimensional matrix codes (e.g., MaxiCode) or stacked codes (e.g., PDF 417), which are also sometimes referred to as “bar codes,” are also used for various purposes. 
     Different methods and types of optical code readers are available for capturing an optical code and for decoding the information represented by the optical code. For example, image-based optical code readers are available that include imagers, such as charge-coupled devices (CCDs) or complementary metal oxide semiconductor (CMOS) imagers, that generate electronic image data that represent an image of a captured optical code. Image-based optical code readers are used for reading one-dimensional optical codes and higher-dimensional optical codes. Because optical codes most often include dark and light patterns (e.g., black and white) that represent binary data, imagers of image-based optical code readers are typically monochrome so that uniform sensitivity for each pixel of the imager is achieved. 
     Common imagers made for image capturing devices, such as still cameras and video cameras, however, are primarily color imagers—not monochrome. Because imagers made for many image capturing devices are color, color imagers are generally made in higher volume and have become more widely available and may be less expensive than monochrome imagers. Common color imagers include a color filter array placed over pixel sensors arranged in a grid of rows and columns. A typical color filter pattern is the Bayer pattern  100  shown in  FIG. 1  and described in U.S. Pat. No. 3,971,065 to Bayer. The Bayer pattern  100  includes red filters (corresponding to red (R) pixels  102 ), green filters (corresponding to green (G) pixels  104 ), and blue filters (corresponding to blue (B) pixels  106 ) that pass, respectively, red, green, and blue wavelengths of light. Each pixel of the color imager outputs data representing only a red, green, or blue intensity value, and, thus, each pixel contains only one-third of the total color data for the pixel location. To create a full-color image, still cameras and video cameras use demosaic algorithms to determine red, green, and blue luminance information for each pixel location. For example, a bilinear interpolation method is known in which a green intensity value is generated for the location of red pixel  102   a  by calculating the mean intensity value of the four adjacent green pixels  104   a ,  104   b ,  104   c  and  104   d . Because the above-described bilinear interpolation method results in visible artifacts, other more complicated interpolation methods have been used for still cameras and video cameras such as bicubic interpolation and adaptive algorithms. 
     Some image-based optical code readers have included color imagers, but the present inventor has recognized that interpolation methods used with color-imager-based optical code readers degrade spatial resolution of optical code images making it difficult to detect accurately edge locations of optical codes. For example, a typical color-imager-based optical code reader that has the same number of total pixels as a monochrome-imager-based optical code reader has lower spatial resolution than the monochrome-imager-based optical code reader due to the color interpolation process. Alternatively, the typical color-imager-based optical code reader will have more total pixels than the monochrome-imager-based optical code reader to compensate for the degrading effects of the color interpolation process. 
     SUMMARY OF THE DISCLOSURE 
     This disclosure describes improved optical code readers and associated methods. 
     One embodiment discloses a method of processing image data that represent light intensity values sensed by different pixels of a color image sensor array used in an optical code reader to improve edge location detection accuracy of optical code elements. The color image sensor array includes first and second sets of pixels. The pixels of the first and second sets are arranged along multiple parallel axes of a first axes group and along multiple parallel axes of a second axes group transverse to the first axes group in a checkerboard pattern. The first set of pixels and the second set of pixels are sensitive to visible wavelength bands of light that are different from one another. The pixels of the first set sense light reflected from an optical code that is positioned within a field of view of the optical code reader. The reflected light forms an image of the optical code on the color image sensor array. The image includes a pattern of dark and light elements that have edges to be detected that are oriented to be appreciably parallel to the axes of the first axes group. A first set of image data representing intensity values sensed by the first set of pixels is produced, and a second set of image data is produced from the first set of image data. The second set of image data represents interpolated intensity values that correspond to locations of selected pixels of the second set. For each location of a selected pixel of the second set, a corresponding interpolated intensity value is produced by using only intensity values sensed by pixels of the first set that share an axis of the first axes group with the selected pixel to thereby preserve spatial resolution along the second axes group and improve location detection accuracy of the edges of the dark and light elements of the optical code. 
     Additional aspects and advantages will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram showing a Bayer pattern of a color filter array. 
         FIG. 2  is a block diagram of an optical code reader according to an embodiment. 
         FIG. 3  is a diagram showing a color image sensor array used in the optical code reader of  FIG. 2 , together with multiple axes corresponding to columns and rows of pixels of the color image sensor array. 
         FIGS. 4 ,  5 , and  6  are examples of different optical codes that may be read by the optical code reader of  FIG. 2 . 
         FIG. 7  shows the pixels of the color image sensor array, together with an image of a portion of an optical code, to demonstrate an interpolation method according to an embodiment. 
         FIG. 8  shows an image of a portion of an optical code superimposed on, and rotated with respect to, the multiple axes of  FIG. 3 . 
         FIG. 9  is a flow chart of the steps of an interpolation method according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     With references to the above-listed drawings, this section describes particular embodiments and their detailed construction and operation. 
       FIG. 2  is a block diagram of an optical code reader  200  according to one embodiment. The optical code reader  200  may be any type of reader, such as, but not limited to, a hand-held type reader, a stationary reader, or a personal digital assistant (PDA) reader. The optical code reader  200  includes a color image sensor array  202  of red pixels  304 , green pixels  306 , and blue pixels  308  shown in  FIG. 3 . The color image sensor array  202  may be a charge-coupled device (CCD), such as a full-frame, frame-transfer, or interline-transfer CCD. Alternatively, the color image sensor array  202  may be a complementary metal oxide semiconductor (CMOS) imager, such as a global shuttered or rolling-reset CMOS imager. 
     The red, green, and blue pixels  304 ,  306 , and  308  of the color image sensor array  202  are arranged along multiple parallel axes of a first axes group  310  and along multiple parallel axes of a second axes group  312  transverse to the first axes group  310  in a Bayer pattern. The green pixels  306  are positioned at every other pixel location to form a checkerboard pattern with a combination of the red pixels  304  and blue pixels  308 .  FIG. 3  illustrates the first and second axes groups  310  and  312  and the pixels  304 ,  306 , and  308  of the color image sensor array  202 . In the color image sensor array  202 , each axis of the first axes group  310  corresponds to a column of red and green pixels  304  and  306  or blue and green pixels  308  and  306 , and each axis of the second axes group  312  corresponds to a row of red and green pixels  304  and  306  or blue and green pixels  308  and  306 . Each row of pixels corresponds to a scan line of the color image sensor array  202 . Alternatively, the first axes group  310  may correspond to the rows of pixels (i.e., the scan lines), and the second axes group  312  may correspond to the columns of pixels.  FIG. 3  shows ten columns (i.e., ten parallel axes of the first axes group  310 ) and eight rows (i.e., eight parallel axes of the second axes group  312 ). The color image sensor array  202 , however, may contain many more columns and rows than the numbers of columns and rows shown in  FIG. 3 . For example, color image sensor array  202  may contain one or more megapixels. 
     The red pixels  304  of color image sensor array  202  are sensitive to visible light having wavelengths that correspond to the color red (wavelengths ranging between about 600 nanometers (nm) and about 750 nm). The green pixels  306  are sensitive to visible light having wavelengths that correspond to the color green (wavelengths ranging between about 500 nm and about 600 nm). The blue pixels  308  are sensitive to visible light having wavelengths that correspond to the color blue (wavelengths ranging between about 400 nm and about 500 nm). The red, green, and blue pixels  304 ,  306 , and  308  produce image data representing light intensities sensed by the pixels. 
     The optical code reader  200  includes an optical system  204  positioned to focus light on the color image sensor array  202 . The optical system  204  may include conventional optical components, such as one or more lenses, an aperture, and, in some cases, a mechanical shutter. As an alternative to a mechanical shutter, the color image sensor array  202  may include electronic shuttering means. 
     The optical code reader  200  may include one or more artificial illumination sources  206  positioned to illuminate a field of view  208  of the optical code reader  200  (two artificial illumination sources are shown in  FIG. 2 ). Alternatively, the optical code reader  200  may rely on ambient light to illuminate the field of view  208  instead of the artificial illumination sources  206 . 
     The optical code reader  200  includes a data processing system  210 . The data processing system  210  may include conventional hardware, such as camera interface hardware, and one or more programmable central processing units (CPU). The data processing system  210  may also include a field programmable gate array (FPGA) for performing various operations described below (e.g., pixel selection, interpolation, edge detection) to reduce the loading of other processor-based stages and, thus, allow for higher pixel rate processing or slower processor speeds. An FPGA may also implement spatial filtering of images using, for example, one-dimensional or two-dimensional convolution kernels for improving a signal-to-noise ratio (by reducing noise using a low-pass or band-pass filter), or for sharpening an image using a high-pass filter. Filters implemented by an FPGA may also be used to compute signal values between pixel sites to produce higher resolution interpolated images. Alternatively, or in addition, to an FPGA, the data processing system may include various types of programmable or non-programmable logic hardware, such as a complex programmable logic device (CPLD), a masked logic array, a standard cell, and a full custom application specific integrated circuit (ASIC), to perform the various operations. 
     The data processing system  210  may be contained within a housing  205  of the optical code reader  200 . Alternatively, the data processing system  210  may be external to the housing  205  of the optical code reader  200 , the data processing system  210  and the optical code reader  200  may communicate through a wired (e.g., EIA232, USB) or wireless (e.g., WLAN, Bluetooth®) communication link, and the data processing system  210  may communicate simultaneously with multiple optical code readers  200 . 
       FIG. 2  shows an optical code  212  in the field of view  208  of the optical code reader  200 . The optical code reader  200  may read any type of optical code such as one-dimensional optical codes and higher-dimensional optical codes.  FIGS. 4 ,  5 , and  6  show three different examples of the types of optical codes that may be read by the optical code reader  200 .  FIG. 4  shows an example of a linear bar code  212   a ;  FIG. 5  shows an example of a stacked code  212   b ; and  FIG. 6  shows an example of matrix code  212   c . Each code  212   a ,  212   b , and  212   c  includes a pattern of dark elements  400  (e.g., bars) and light elements  402  (e.g., spaces). The dark and light elements  400  and  402  include demarcation edges  404  to be detected by the optical code reader  200 . With respect to the matrix code  212   c , in addition to detecting the demarcation edges  404 , dark cells  400   a  and light cells  402   a  are to be classified as dark or light without the need to precisely locate the edges of each cell. It may be useful, however, to locate the edges of at least some of the dark and light cells  400   a  and  402   a  to provide additional information for identifying the locations of at least some of the cells. 
     In operation, the optical code  212  is positioned in, or passed through, the field of view  208  of the optical code reader  200 . The field of view  208  of the optical code reader  200  may be illuminated by artificial light generated by artificial illumination sources  206  or by ambient light. The light that illuminates the field of view  208  includes light having wavelengths that correspond to the color green. The light reflects off the optical code  212  toward the optical system  204 . The optical system  204  focuses the reflected light on the pixels of the color image sensor array  202 —the focused light forming an image of the optical code  212  on the pixels. A first set of image data representing the intensities values of light sensed by the green pixels  306  is transmitted to the data processing system  210 . The data processing system  210  uses the first set of image data to produce a second set of image data representing interpolated intensity values for red and blue pixel locations. The first and second sets of image data are used to decode the optical code  212  (e.g., detect the edges  404  of the dark elements  400  and light elements  402 , classify cells as dark or light). The red pixels  304  and the blue pixels  308  may also produce image data representing the intensities values of light sensed by the red and blue pixels  304  and  308 . The image data produced by the red and blue pixels  304  and  308 , however, may be replaced by the second set of image data or ignored. The image data produced by the red and blue pixels  304  and  308  may be retained for other purposes such as providing a color image on a display for human viewing. 
     Orientations of the optical code  212  and the parallel axes of the first and second axes groups  310  and  312  will now be described.  FIG. 7  shows a portion of the pixels  304 ,  306 , and  308  of the color image sensor array  202  and an image  212 ′ of a portion of the optical code  212  that is formed on the color image sensor array  202 . For clarity, the image  212 ′ is shown to the side of—rather than over—the pixels  304 ,  306 , and  308 . The optical code  212  is positioned in the field of view  208  so that edges  404 ′ of the image  212 ′ are appreciably parallel to the axes of the first axes group  310  (i.e., the columns of the pixels). The edges  404 ′ being “appreciably parallel” to the axes of the first axes group  310  means that the smallest angle θ between the edges  404 ′ and the axes of the first axes group  310  is less than the smallest angle φ between the edges  404 ′ and the axes of the second axes group  312  as shown in  FIG. 8 . In other words, “appreciably parallel” includes any orientation in which the smallest angle θ between the edges  404 ′ and the axes of the first axes group  310  ranges from about 0° to about 45°. The optical code  212  may be positioned in the field of view  208  to achieve the above-described alignment by moving (e.g., rotating) the optical code  212  and/or the optical code reader  200 . 
     Production of the interpolated intensity values according to one embodiment will now be described in more detail. The interpolated intensity values correspond to selected locations of the red pixels  304  and the blue pixels  308 . For a selected location, an interpolated intensity value is produced using only the intensity values of the green pixels  306  located along the axis of the first axes group  310  (i.e., the intensity values of the green pixels  306  that share an axis of the first axes group  310  with the selected location). In other words, the data processing system  210  performs single-axis interpolation—the axis of interpolation corresponding to the orientation of the edges  404 ′ of the image  212 ′ of the optical code  212 . For example,  FIG. 7  shows arrows  500  representing the intensity values of the green pixels  306  that are used to produce the interpolated intensity values for different locations of the red and blue pixels  304  and  308 . For the red pixel  304  located in the second column and third row, the intensity values of the green pixels  306  located above (i.e., second column and second row) and below (i.e., second column and fourth row) are used to produce the interpolated intensity value for the red pixel location. The same interpolation method is used to produce interpolated intensity values for red and blue pixels locations along consecutive rows (i.e., along consecutive axes of the second axes group  312 ). 
     By using only the intensity values of green pixels  306  located along axes appreciably parallel to the edges of optical codes to produce interpolated intensity values, the locations of the edges of optical codes are preserved more accurately compared to methods that interpolate across the edges of optical codes. In other words, the above-described method preserves spatial resolution of the color image sensor array  202  along the parallel axes of the second axes group  312  so that the locations of the edges of optical codes may be accurately identified. Thus, the above-described method allows the color image sensor array  202  to achieve a spatial resolution across optical code edges that is comparable to that achieved by a monochrome imager having the same number of pixels as the sum of the red, green, and blue pixels  304 ,  306 , and  308 . In addition, by using only information from the green pixels  306  to read the optical code  212 , problems associated with, and the need to compensate for, differences in sensitivity between the red pixels  304 , green pixels  306 , and blue pixels  308  may be avoided. 
       FIG. 9  is a flow chart of steps of a particular method  700  that may be implemented by the optical code reader  200 . After receiving the image data from the green pixels  306 , together with the image data from the red and blue pixels  304  and  308 , the data processing system  210  selects a pixel by identifying a portion of the image data that corresponds to the selected pixel (step  702 ). The data processing system  210  identifies whether the selected pixel is a green pixel  306  (step  704 ). If the selected pixel is a green pixel  306 , the intensity value of the green pixel  306  is preserved to decode the optical code  212  (step  706 ). 
     If the selected pixel is a red pixel  304  or a blue pixel  308 , the intensity value is ignored or discarded, and the data processing system  210  identifies the neighboring green pixels  306  that share an axis of the first axes group  310  with the selected pixel (step  708 ). For example, for the blue pixel  308  in the first column and second row of  FIG. 7 , the green pixels  304  above and below it (i.e., the green pixel  306  in the first column, first row and the green pixel  304  in the first column, third row) are used. The data processing system  210  uses the intensity values of the neighboring green pixels  306  to calculate a mean intensity value of the intensity values of the neighboring green pixels  306  (step  710 ) (e.g., mean intensity value=(I 1 +I 2 )/2, where I 1  is the intensity value of the first neighboring green pixel  306  and I 2  is the intensity value of the second neighboring green pixel  306 ). The mean intensity value is used as the interpolated intensity value for the selected pixel (step  712 ). The above-described steps are repeated for each pixel (represented by dashed lines extending from steps  706  and  712  to step  702 ), and the green pixel values of the green pixels  306  and the interpolated intensity values of the red and blue pixel locations are collectively used to decode the optical code  212 . 
     The data processing system  210  need not perform all the above-described steps, and the data processing system  210  may perform alternative steps. For example, although linear interpolation has been described, the data processing system  210  may perform other known interpolation methods using one or more green pixels  306  located along the parallel axes of the first axes group  310  (i.e., other more computationally complex interpolation methods may be used to achieve higher levels of accuracy such as polynomial or spline interpolation). Additional information about various interpolation methods can be found in W ILLIAM  K. P RATT , D IGITAL  I MAGE  P ROCESSING,  113-116 (John Wiley &amp; Sons 1978) and Erik Meijering,  A Chronology of Interpolation: From Ancient Astronomy to Modern Signal and Image Processing,  90 P ROCEEDINGS OF THE  IEEE 319-42 March 2002, the entire contents of which are incorporated herein by reference. 
     For some locations of the red and blue pixels  304  and  308 , interpolated intensity values may not be produced (i.e., some locations are not selected locations). For example, for a color image sensor array  202  with n parallel axes of the second axes group  312  (i.e., n rows), interpolated intensity values need not be produced for red or blue pixels located along the first axis of the second axes group  312  (e.g., the top edge row of the color image sensor array  202 ) and the nth axis of the second axes group (e.g., the bottom edge row of the color image sensor array  202 ). In one embodiment, the intensity values of the green pixels  306  are the only intensity values used from the top edge and bottom edge rows to read the optical code  212 . Alternatively, the intensity values of the top edge and bottom edge rows may not be used to decode the optical code  212 . 
     Although the system and method described above correspond to an example where the image  212 ′ of the optical code  212  is oriented in a picket fence orientation, the image  212 ′ of the optical code  212  may also be oriented in a ladder orientation. With the ladder orientation, the parallel axes of the first axes group  310  correspond to the rows of pixels and interpolation is performed using the green pixels  306  of the rows instead of the green pixels of the columns so that spatial resolution in a direction across the edges  404 ′ of the image  212 ′ may be preserved. 
     In an alternative embodiment, the single-axis interpolation described above is performed along the axes of the first axes group  310  and then along the axes of the second axes group  312  so that two sets of interpolated intensity values are produced. The two sets of interpolated intensity values may be produced from the same set of green intensity values (e.g., a single frame) or from different sets of green intensity values (e.g., different frames). The sets of interpolated intensity values are used independently, in combination with the corresponding set of green intensity values, to identify the edges of the optical code  212  and, thereby, decode it. In other words, two interpolated images (including green intensity values and interpolated intensity values) are computed, one using vertical interpolation and the other using horizontal interpolation, and each interpolated image may be decoded independently. 
     For example, after capturing an image of the optical code  212 , in a first processing round, the data processing system  210  performs the method  700  for each pixel of the color image sensor array  202  (note, the pixels of the first and nth rows need not be processed); then, in a second processing round, the data processing system  210  performs the method  700  for each pixel of the color image sensor array (again, the pixels of the first and nth rows need not be processed) with the exception that in steps  708  and  710 , the neighboring green pixels  306  that share an axis of the second axes group  312  with the selected pixel are identified and their intensity values are used to produce the mean intensity value (e.g., for the red pixel  304  in the second column, third row of  FIG. 7 , the green pixels  306  to the right and left of it are used). Alternatively, instead of performing two processing rounds (one for vertical interpolation and one for horizontal interpolation), one processing round may be performed in which a vertical interpolation value and a horizontal interpolation value are created for a blue or red pixel location before processing a next pixel. The data processing system  210  may then attempt to decode the optical code  212  using one of the two interpolated images or both interpolated images. 
     By independently generating two interpolated images—one reflecting horizontal interpolation and the other reflecting vertical interpolation—locations of the edges  404  of the optical code  212  may be accurately identified regardless of the orientation of the optical code  212  (e.g., picket fence or ladder). Moreover, the optical code reader  200  need not attempt to determine the orientation of the optical code  212  prior to interpolating and decoding. For example, labels on packages and/or other optical codes within the field of view  208  of the optical code reader  200  may make it difficult for the optical code reader  200  to determine, prior to decoding, the orientation of the optical code  212  (e.g., the edges of symbols on packaging labels may dominate over those of the optical code  212  when attempting to use conventional edge detection techniques (e.g., gradient computation) to determine the orientation of the optical code  212 ). The data processing system  210  may operate relatively quickly so that it generates the interpolated images and attempts to decoded them without significantly increasing the processing time. 
     Alternatively, the data processing system  210  may analyze the interpolated images to determine which interpolated image to decode. The interpolated images may be compared (e.g., edge sharpness comparison) to identify the interpolated image that most accurately reflects the locations of the optical code edges  404 . Several possible measures of the amount of signal modulation may be implemented to choose the interpolated image that is most likely to have been interpolated in a direction substantially parallel to the edges  404  of the optical code  212 . For example, one measurement may be standard deviation, in which the image is chosen that has the smallest standard deviation of pixel values along a row or column. Alternatively, a second measurement may be the differences between adjacent pixels, in which the image is chosen that has the smallest sum of the absolute values of the differences between adjacent pixels. The interpolated image that represents edges  404  in higher resolution may be selected for decoding. Choosing one of the interpolated images to decode—instead of decoding both interpolated images—may be useful in some applications. 
     The data processing system  210  may also use feedback information from a decoder implemented in the data processing system  210  to identify the orientation of the optical code  212  so that the optical code reader  200  can improve its performance during a subsequent read. For example, the decoder may be able to identify whether the optical code  212  is oriented in a picket fence or ladder orientation without being able to actually decode it. Accordingly, the decoder can communicate that the optical code  212  was in a particular orientation so that the data processing system  210  need only interpolate along either the first set of axes  310  or the second set of axes  312  during a subsequent attempt to read the optical code  212 . 
     In the embodiments described above, green pixel data is used to compute interpolated intensity values and to decode the optical code  212 . Skilled persons will recognize, however, that red pixel data, blue pixel data, or a combination of red and blue pixel data may be used in accordance with the above-described embodiments to compute interpolated intensity values corresponding to green pixel locations to decode the optical code  212 . 
     Moreover, although the colors red, green, and blue and the Bayer pattern have been described above in connection with the color image sensor array  202 , other colors and filter patterns may be used without departing from the scope and spirit of the present disclosure. For example, the color image sensor array  202  may include a cyan, yellow, green, and magenta (CYGM) filter; a red, green, blue, and emerald (RGBE) filter; or a white, red, green, and blue (WRGB) filter. The optical code reader  200  and its associated methods are flexible to compensate for the effects of these various filters. For example, single-axis interpolation may be conducted using one or more of the colors of these different filters. 
     Certain embodiments may be capable of achieving one or more of the following advantages: (1) enabling utilization of lower cost color imagers in optical code readers; (2) achieving higher spatial resolution with a color imagers by interpolating along—not across—optical code edges; (3) reducing processor loading to allow for higher pixel rate processing or lower processor speeds by performing various operations via an FPGA; and (4) reading higher density optical codes than would otherwise be possible. 
     Though the present invention has been set forth in the form of its preferred embodiments, it is nevertheless intended that modifications to the disclosed systems and methods may be made without departing from inventive concepts set forth herein.