To improve the performance of sparsity-based single image super-resolution (SR), we propose a joint SR framework of structure prior based sparse representation (spsr). The proposed spsr algorithm exploits the multi-sc...
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ISBN:
(纸本)9781479957521
To improve the performance of sparsity-based single image super-resolution (SR), we propose a joint SR framework of structure prior based sparse representation (spsr). The proposed spsr algorithm exploits the multi-scale spatial structural self-similarities, the gradient prior and nonlocally centralized sparse representation to formulate a constrained optimization problem for high-resolution image recovery. The high-resolution image is firstly initialized by exploiting cross-scale patch redundancy in an image pyramid from single input low-resolution image. Then the sparse modeling of the image SR problem is proposed to refine it further, where the gradient histogram preservation is incorporated as a regularization term. Finally, an iterative solution is provided to solve the problem of model parameter estimation and sparse representation. Experimental results on image super-resolution validate the generality, effectiveness and robustness of the proposed spsr algorithm.
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