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Sparse representation models code an image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful...
Important structures in photographic images such as edges and textures are jointly characterized by local variation and nonlocal invariance (similarity). Both of them provide valuable heuristics to the regularization of image restoration process. In this paper, we propose to explore two sets of complementary ideas: 1) locally learn PCA-based dictionaries and estimate the sparsity regularization parameters...
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