Super-resolution Reconstruction Algorithm Based on Non-local Simultaneous Sparse Approximation
Li Min①③ Li Shi-hua① Li Xiao-wen① Le Xiang②
①(Institute of Geo-Spatial Information Science and Technology,University of Electronic Science and Technology of China, Chengdu 611731, China) ②(School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China) ③(Department of Scientific Research, Guilin Airforce Academy, Guilin 541003, China)
Abstract 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.
2011, Vol. 33 
