Learning the Kernel Matrix for Superresolution (original) (raw)

2006 IEEE Workshop on Multimedia Signal Processing, 2006

Abstract

This paper proposes the application of learned kernels in support vector regression to superresolution in the discrete cosine transform (DCT) domain. Though previous works involve kernel learning, their problem formulation is examined to reformulate the semi-definite programming problem of finding the optimal kernel matrix. For the particular application to superresolution, downsampling properties derived in the DCT domain are exploited to

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