Global hard thresholding algorithms for joint sparse image representation and denoising

27 May 2017 Reza Borhani Jeremy Watt Aggelos Katsaggelos

Sparse coding of images is traditionally done by cutting them into small patches and representing each patch individually over some dictionary given a pre-determined number of nonzero coefficients to use for each patch. In lack of a way to effectively distribute a total number (or global budget) of nonzero coefficients across all patches, current sparse recovery algorithms distribute the global budget equally across all patches despite the wide range of differences in structural complexity among them... (read more)

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