Methods for background and noise suppression in binary to grayscale image conversion

Abstract

One embodiment is a method for suppressing background inaccuracies in binary to grayscale image conversion. A binary image is converted to a grayscale image using a neighbor map. An image enhancement function is applied to the grayscale image to supress background inaccuracies in the grayscale image. Another embodiment is method for converting a binary pixel of a binary image to a grayscale pixel of a grayscale image and suppressing noise in the grayscale image using selective filtering of the binary image. Another embodiment is a method for converting a binary image to a first grayscale image and suppressing noise in the first grayscale image to produce a noise suppressed grayscale image using selective filtering of the grayscale image.

1 . A method for converting a binary pixel of a binary image to a grayscale pixel of a grayscale image and suppressing background inaccuracies in the grayscale image, comprising:
 inputting the binary image;   associating the binary pixel with a neighbor map;   converting the binary pixel to the grayscale pixel based on the neighbor map; and   applying an image enhancement function to the grayscale pixel to suppress background inaccuracies in the grayscale image.   
     
     
         2 . The method of  claim 1 , wherein the binary pixel comprises a one bit value and the grayscale pixel comprises an eight bit value. 
     
     
         3 . The method of  claim 1 , wherein converting the binary pixel to the grayscale pixel based on the neighbor map comprises:
 comparing the neighbor map to one or more binary patterns of a look up table; and   assigning a grayscale value corresponding to a binary pattern of the look up table matching the neighbor map to the grayscale pixel.   
     
     
         4 . The method of  claim 1 , wherein converting the binary pixel to the grayscale pixel based on the neighbor map comprises filtering the neighbor map. 
     
     
         5 . The method of  claim 1 , wherein the image enhancement function comprises a tone reproduction curve. 
     
     
         6 . The method of  claim 1 , wherein the image enhancement function comprises a continuous function. 
     
     
         7 . The method of  claim 1 , wherein the image enhancement function comprises a discontinuous function. 
     
     
         8 . A method for converting a binary pixel of a binary image to a grayscale pixel of a grayscale image and suppressing noise in the grayscale image, comprising:
 inputting the binary image;   associating the binary pixel with a neighbor map;   converting the neighbor map to a weighting value;   filtering the neighbor map to produce a first grayscale pixel value;   comparing the neighbor map to one or more binary patterns of a conversion look up table to produce a second grayscale pixel value;   multiplying an inverted weighting value by the first grayscale pixel value to produce a first weighted grayscale pixel value;   multiplying the weighting value by the second grayscale pixel value to produce a second weighted grayscale pixel value; and   summing the first weighted grayscale pixel value and the second weighted grayscale pixel value to produce the grayscale pixel and suppress noise.   
     
     
         9 . The method of  claim 8 , wherein the binary pixel comprises a one bit value, the first grayscale pixel value comprises an eight bit value, and the second grayscale pixel value comprises an eight bit value. 
     
     
         10 . The method of  claim 8 , wherein the weighting value comprises a value greater than or equal to zero and less than or equal to one. 
     
     
         11 . The method of  claim 10 , wherein the inverted weighting value comprises the difference between the weighting value and one. 
     
     
         12 . The method of  claim 8 , wherein the weighting value comprises one of one and zero. 
     
     
         13 . The method of  claim 8 , wherein converting the neighbor map to a weighting value comprises:
 comparing the neighbor map to one or more binary patterns of a control look up table; and   producing a weighting value corresponding to a binary pattern of the control look up table matching the neighbor map.   
     
     
         14 . The method of  claim 8 , wherein converting the neighbor map to a weighting value comprises:
 summing the values of the neighbor map to produce a neighbor map value;   comparing the neighbor map value to a threshold value;   assigning one to the weighting value, if the neighbor map value is greater than the threshold value; and   assigning zero to the weighting value, if the neighbor map value is less than or equal to the threshold value.   
     
     
         15 . The method of  claim 8 , wherein converting the neighbor map to a weighting value comprises:
 summing the values of the neighbor map to produce a neighbor map value;   comparing the neighbor map value to a threshold value;   assigning zero to the weighting value, if the neighbor map value is greater than the threshold value; and   assigning one to the weighting value, if the neighbor map value is less than or equal to the threshold value.   
     
     
         16 . A method for converting a binary image to a first grayscale image and suppressing noise in the first grayscale image to produce a noise suppressed grayscale image, comprising:
 inputting the binary image;   associating each binary pixel of the binary image with a binary neighbor map;   converting the each binary pixel to an each first grayscale pixel of the first grayscale image based on the binary neighbor map;   creating an each grayscale neighbor map for the each first grayscale pixel;   converting the each grayscale neighbor map to an each selection value;   filtering the each grayscale neighbor map to produce an each second grayscale pixel; and   producing the noise suppressed grayscale image by selecting an each noise suppressed grayscale pixel as the each first grayscale pixel if the each selection value is a first selection value and selecting an each noise suppressed grayscale pixel as the each second grayscale pixel if the each selection value is a second selection value.   
     
     
         17 . The method of  claim 16 , wherein the each binary pixel comprises a one bit value, the each first grayscale pixel comprises and eight bit value, and the each second grayscale pixel comprises an eight bit value. 
     
     
         18 . The method of  claim 16 , wherein converting an each binary pixel to an each first grayscale pixel based on the binary neighbor map comprises:
 comparing the binary neighbor map to one or more binary patterns of a look up table; and   assigning a grayscale value corresponding to a binary pattern of the look up table matching the binary neighbor map to the each first grayscale pixel.   
     
     
         19 . The method of  claim 16 , wherein converting the each grayscale neighbor map to an each selection value comprises:
 comparing the each first grayscale pixel of each neighbor in the each grayscale neighbor map to a threshold value; and   assigning the second selection value to the each selection value if the each first grayscale pixel of all neighbors in the each grayscale neighbor map are greater than the threshold value.   
     
     
         20 . The method of  claim 16 , wherein converting the each grayscale neighbor map to an each selection value comprises:
 comparing the each first grayscale pixel of each neighbor in the each grayscale neighbor map to a threshold value; and   assigning the first selection value to the each selection value if the each first grayscale pixel of all neighbors in the each grayscale neighbor map are less than the threshold value.