Apparatus and method of building an electronic database for resolution synthesis

Abstract

An electronic database for image interpolation is generated by a computer. The computer generates a low-resolution image from a training image, a plurality of representative vectors from the low-resolution image, and a plurality of interpolation filters corresponding to each of the representative vectors. The interpolation filters and the representative vectors are generated off-line and can be used to perform image interpolation on an image other than the training image. The database can be stored in a device such as computer or a printer.

What is claimed is:  
     
         1 . A method of building an electronic database for data resolution synthesis from at least one training file, the method comprising the steps of: 
 generating a low-resolution file from each training file;    generating a plurality of representative vectors from each low-resolution file; and    generating a set of interpolation filters for each of the representative vectors;    whereby the interpolation filters and the representative vectors can be used to perform data resolution synthesis on a file other than the training file.    
     
     
         2 . The method of  claim 1  wherein the representative vectors are generated by computing a number NCV of cluster vectors from each low-resolution file and using the cluster vectors to compute the representative vectors; and wherein low-resolution observation vectors, the cluster vectors, the representative vectors and a high-resolution file corresponding to each low-resolution file are used to compute the interpolation filters, whereby a high resolution file may be a training file.  
     
     
         3 . The method of  claim 2 , further comprising the step of generating a sharpened high-resolution file, the sharpened high-resolution file being used to compute the interpolation filters.  
     
     
         4 . The method of  claim 2 , wherein the representative vectors ar generated by using a maximum likelihood estimate.  
     
     
         5 . The method of  claim 4 , wherein the vectors are generated by using an expectation maximization technique.  
     
     
         6 . The m thod of  claim 4 , wherein a classifier including the representative v ctors is computed by initializing th classifier and updating the classifier until optimal values for the classifier have been obtained.  
     
     
         7 . The method of  claim 6 , wherein the classifier further includ s a variance and a number M of class weights, and wherein the representative vectors, the class weights and the variance are computed simultaneously.  
     
     
         8 . The method of  claim 2 , wherein each cluster vector is generat d by forming an observation window about sampled data in a low resolution file, extracting a vector including neighboring data of the sampled data, and scaling the vector.  
     
     
         9 . The method of  claim 2 , wherein coefficients for the interpolation filters are computed by: 
 computing a number NFDV of filter design triplets from data in the low-resolution file, where NFDV is a positive integer, each filter design triplet corresponding to sampled data in the low-resolution file, each filter design triplet including an observation vector for the sampled data, a cluster vector for the sampled data, and a vector of high resolution data from a high-resolution file;    computing training statistics from the filter design triplets; and    computing the coefficients from the training statistics.    
     
     
         10 . The method of  claim 2 , wherein the steps are run off-line in a computer.  
     
     
         11 . The method of  claim 1 , wherein the interpolation filters are linear filters.  
     
     
         12 . The m thod of  claim 1 , wherein the representative vectors are generated by using a parameter optimization technique.  
     
     
         13 . A method of using a computer to compute a plurality of resolution synthesis parameters from a training image, the method comprising the steps of: 
 computing a low-resolution image from the training image;    computing a plurality of cluster vectors for a number NCV of pixels in the low-resolution image, where NCV is a positive integer;    using the cluster vectors to compute a number M of representative vectors for the low resolution image, where M is a positive integer that is less than NCV; and    using low-resolution observation vectors, the cluster vectors, the representative vectors and vectors from a high-resolution image to compute sets of interpolation filter coefficients corresponding to each of the representativ vectors;    whereby the high-resolution image may be the training image; and    whereby the interpolation filter coefficients and the number M of representative vectors are stored in the database for later interpolation of an image other than the training image.    
     
     
         14 . The method of  claim 13 , wherein the number NCV is between 25,000 and 100,000, whereby between 25,000 and 100,000 cluster vectors are computed.  
     
     
         15 . The method of  claim 13 , wherein each cluster vector for a non-border pixel is computed by extracting a first vector from a square observation window centered about a sampled pixel in the low-resolution image, and scaling the first vector.  
     
     
         16 . Th method of  claim 13 , where the number M of r pres ntative vectors is between 50 and 100.  
     
     
         17 . The method of  claim 13 , wherein the representative vectors are computed using a maximum likelihood estimate.  
     
     
         18 . The method of  claim 17 , wherein a classifier including th representative vectors is computed by initializing the classifier and updating the classifier until optimal values for the classifier have been obtained.  
     
     
         19 . The method of  claim 13 , wherein the representative vectors are computed using an expectation-maximization algorithm.  
     
     
         20 . The method of  claim 19 , wherein the representative vectors are computed by: 
 setting initial values for a classifier including a number M of class weights, a variance and the number M of representative vectors;    computing a quality measure of how well the cluster vectors are represented by the initial values for the classifier;    updating the classifier;    recomputing the quality measure for the updated classifier; and    determining whether the cluster vectors are suitably represented by th updated classifier, the classifier being updated until the cluster vectors are suitably represented.    
     
     
         21 . The method of  claim 13 , further comprising the st p of computing a sharpened high-resolution image from the training image, wherein the sharpened image is used along with low-resolution observation vectors, th cluster vectors and the representative vectors to compute the interpolation filter coefficients.  
     
     
         22 . The method of  claim 13 , wherein the interpolation filter coefficients are computed by: 
 computing a number NFDV of filter design triplets from the low-resolution image, where NFDV is a positive integer, each filter design triplet corresponding to a sampled pixel in the low-resolution image, each filter design triplet including an observation vector for the sampled pixel, a cluster vector for the sampled pixel, and a vector of high-resolution pixels corresponding to the sampled pixel, the high-resolution pixels being taken from the high-resolution image;    computing training statistics from the filter design triplets; and    computing the coefficients from the training statistics.    
     
     
         23 . The method of  claim 22 , wherein the number NFVD of filter design triplets is between 500,000 and 1,000,000, whereby between 500,000 and 1,000,000 filter design triplets are computed.  
     
     
         24 . The method of  claim 22 , wherein the interpolation filter coefficients are computed for linear interpolation filters.  
     
     
         25 . The method of  claim 13 , wherein the steps are run off-line in the computer.  
     
     
         26 . The method of  claim 25 , wherein the database is stored for transfer to a second computer, whereby the second computer can access the database to perform image interpolation on images other than the training images.  
     
     
         27 . The method of  claim 25 , wherein the database is stored in memory of a printer, whereby the printer can access the database to p rform image interpolation on images other than the training images.  
     
     
         28 . The method of  claim 13 , wherein the repres ntativ vectors are generated by using a parameter optimization technique.  
     
     
         29 . Apparatus comprising: 
 a processor; and    memory means for storing an electronic database and a plurality of executable instructions, the instructions, when executed, instructing the processor to access a training file; generate a low-resolution file from the training fil; generate a plurality of representative vectors from the low-resolution file; generate a set of interpolation filters for each of the representative vectors; and store the interpolation filters and the representative vectors in the memory means as part of the database.    
     
     
         30 . The apparatus of  claim 29 , wherein the instructions instruct the processor to generate the representative vectors by computing a number NCV of cluster vectors from the low-resolution file, and using the cluster vectors to generate the representative vectors; and wherein the instructions instruct the processor to generate the interpolation filters from low-resolution observation vectors, the cluster vectors, the representative vectors and a plurality of vectors from a high-resolution file corresponding to the low-resolution file.  
     
     
         31 . The apparatus of  claim 30 , wherein the instructions further instruct the processor to generate a sharpened high-resolution file from the training file, the sharpened high-resolution file being used to comput the interpolation filters.  
     
     
         32 . The apparatus of  claim 30 , wherein the instructions instruct the processor to generate a classifier including the representative vectors by initializing the classifier and updating the classifier until optimal values for the classifier have been obtained.  
     
     
         33 . The apparatus of  claim 30 , wherein the instructions instruct the processor to generate each cluster vector by forming an observation window about sampled data in the low-resolution file, extracting a vector including neighboring data of the sampled data, subtracting a value of the sampled data from values of the data in the vector; and scaling the vector.  
     
     
         34 . The apparatus of  claim 30 , wherein the instructions instruct the processor to compute coefficients for the interpolation filters by: 
 computing a number NFDV of filter design triplets from data in the low-resolution file, where NFDV is a positive integer, each filter design triplet corresponding to sampled data in the low-resolution file, each filter design triplet including an observation vector for the sampled data, a cluster vector for th sampled data, and a vector of high resolution data from a high-resolution fil, the high resolution data corresponding to the sampled data;    computing training statistics from the filter design triplets; and    computing the coefficients from the training statistics.    
     
     
         35 . The apparatus of  claim 30 , wherein the interpolation filters are linear filters.  
     
     
         36 . An article of manufacture for instructing a processor to compute a resolution synthesis database from a training image, the article comprising: 
 computer memory; and    a plurality of executable instructions stored in the computer memory, the instructions, when executed, instructing the processor to compute a low-resolution image from the training image; compute a plurality of representative vectors from th low-resolution image; and comput a s t of int rpolation filt rs for ach of the representative vectors; whereby the interpolation filters and the repres ntative vectors form a part of the database.    
     
     
         37 . The article of  claim 36 , wherein the instructions instruct the processor to compute the representative vectors by computing a number NCV of cluster vectors from the low-resolution image, and using the cluster vectors to compute the representative vectors; and wherein the instructions instruct the processor to compute the interpolation filters from low-resolution observation vectors, the cluster vectors, the representative vectors and vectors from a high-resolution image corresponding to the low-resolution image.  
     
     
         38 . The article of  claim 37 , wherein the instructions further instruct the processor to compute a sharpened high-resolution image from the training image, the sharpened high-resolution file being used to compute the interpolation filters.  
     
     
         39 . The article of  claim 37 , wherein the instructions instruct the processor to compute a classifier including the representative vectors by initializing the classifier and updating the classifier until optimal values for the classifier have been obtained.  
     
     
         40 . The article of  claim 37 , wherein the instructions instruct the processor to compute each cluster vector by forming an observation window about a sampled pixel in the low-resolution image, extracting a vector including neighboring pixels of the sampled pixel, subtracting a value of the sampled pixel from values of the pixels in the vector; and scaling the vector.  
     
     
         41 . The article of  claim 37 , wherein the instructions instruct the processor to compute coefficients for the interpolation filters by: 
 computing a numb r NFDV of filter design triplets from pix Is in th low-resolution image, where NFDV is a positive integ r, ach fift r design triplet corresponding to a sampled pixel in the low-resolution image, each filter design triplet including an observation vector for the sampled pixel, a cluster vector for the sampled pixel, and a vector of high resolution pixels from a high-resolution image, the high resolution pixels corresponding to the sampled pixel;    computing training statistics from the filter design triplets; and    computing the coefficients from the training statistics.    
     
     
         42 . The article of  claim 36 , wherein the representative vectors are generated by using a parameter optimization technique.  
     
     
         43 . An article of manufacture comprising: 
 computer memory; and    a database encoded in the computer memory, the database including a plurality of sets of resolution synthesis parameters, each set corresponding to an interpolation factor, each set including a classifier and a number M of resolution synthesis filters, each classifier including a number M of representative vectors, where M is a positive integer.    
     
     
         44 . The article of  claim 43 , wherein each classifier further includes a variance and a number M of class weights.  
     
     
         45 . The article of  claim 43 , wherein the number M is between 50 and 100.