Apparatus and method of building an electronic database for resolution synthesis
First Claim
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1. A method of building an electronic database for data resolution synthesis from at least one training file, the method comprising:
- generating a low-resolution file from each training file;
generating a plurality of representative vectors from each low-resolution file, the representative vectors generated by computing a number of cluster vectors from each low-resolution file and using the cluster vectors to compute the representative vectors; and
generating a set of interpolation filters for each of the representative vectors;
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.
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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.
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Citations
39 Claims
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1. A method of building an electronic database for data resolution synthesis from at least one training file, the method comprising:
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generating a low-resolution file from each training file;
generating a plurality of representative vectors from each low-resolution file, the representative vectors generated by computing a number of cluster vectors from each low-resolution file and using the cluster vectors to compute the representative vectors; and
generating a set of interpolation filters for each of the representative vectors;
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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
computing a number of filter design triplets from data in the low-resolution file, 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.
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9. The method of claim 1, wherein the steps are run off-line in a computer.
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10. The method of claim 1, wherein the interpolation filters are linear filters.
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11. The method of claim 1, wherein the representative vectors are generated by using a parameter optimization technique.
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12. A method of using a computer to compute a plurality of resolution synthesis parameters from a training image, the method comprising the steps of:
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computing a low-resolution image from the training image;
computing a plurality of cluster vectors for a number of pixels in the low-resolution image;
using the cluster vectors to compute a number of representative vectors for the low resolution image, where that the number of representative vectors is less than the number of cluster vectors; 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 representative vectors;
whereby the high-resolution image may be the training image; and
whereby the interpolation filter coefficients and the representative vectors are stored in the database for later interpolation of an image other than the training image. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
setting initial values for a classifier including a number of class weights, a variance and the number 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 the updated classifier, the classifier being updated until the cluster vectors are suitably represented.
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20. The method of claim 12, further comprising the step of computing a sharpened high-resolution image from the training image, wherein the sharpened image is used along with low-resolution observation vectors, the cluster vectors and the representative vectors to compute the interpolation filter coefficients.
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21. The method of claim 12, wherein the interpolation filter coefficients are computed by:
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computing a number of filter design triplets from the low-resolution image, 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.
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22. The method of claim 21, wherein between 500,000 and 1,000,000 filter design triplets are computed.
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23. The method of claim 21, wherein the interpolation filter coefficients are computed for linear interpolation filters.
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24. The method of claim 12, wherein the steps are run off-line in the computer.
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25. The method of claim 24, 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.
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26. The method of claim 24, wherein the database is stored in memory of a printer, whereby the printer can access the database to perform image interpolation on images other than the training images.
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27. The method of claim 12, wherein the representative vectors are generated by using a parameter optimization technique.
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28. Apparatus comprising:
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a processor; and
memory for storing an electronic database, the memory programmed to cause the processor to access a training file;
generate a low-resolution file from the training file;
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 as part of the database;
the processor generating the representative vectors by computing a number of cluster vectors from the low-resolution file, and using the cluster vectors to generate the representative vectors;
the processor generating 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. - View Dependent Claims (29, 30, 31, 32, 33)
computing a number of filter design triplets from data in the low-resolution file, 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, 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.
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33. The apparatus of claim 28, wherein the interpolation filters are linear filters.
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34. An article for instructing a processor to compute a resolution synthesis database from a training image, the article comprising:
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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 the low-resolution image; and
compute a set of interpolation filters for each of the representative vectors;
the processor computing the representative vectors by computing a number of cluster vectors from the low-resolution image, and using the cluster vectors to compute the representative vectors;
the processor computing 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. - View Dependent Claims (35, 36, 37, 38, 39)
computing a number of filter design triplets from pixels in the low-resolution image, 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 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.
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39. The article of claim 34, wherein the representative vectors are generated by using a parameter optimization technique.
Specification