Image Super-Resolution Based on Structure-Modulated Sparse Representation
Abstract— Image Super-Resolution Based on Structure-Modulated Sparse Representation. Sparse representation has recently attracted enormous interests in the ﬁeld of image restoration. The conventional sparsity-based < Final Year Projects 2016 > methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for higher solution image recovery.
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