Super-resolution Reconstruction Algorithm Based on Non-local Simultaneous Sparse Approximation

A novel super-resolution reconstruction method based on non-local simultaneous sparse approximation is presented, which combines simultaneous sparse approximation method and non-local self-similarity. The sparse association between high- and low-resolution patches pairs of cross-scale self-similar sets via simultaneous sparse coding is defined, and the association as a priori knowledge is used for super-resolution reconstruction. This method keeps the patches pairs the same sparsity patterns, and makes efficiently use of the self-similar information. The adaptability is enhanced. Several experiments using nature images show that the presented method outperforms other several learning-based super-resolution methods.