Exploiting multi-scale spatial structures for sparsity based single image super-resolution

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.