Sparse representation based super resolution using saliency and edge information

Sparse representation provides effective prior information for single-frame super resolution reconstruction. The diversified training samples of the general dictionary lead to the difficulty of recovering fine grained details due to the negligence of redundant structural characteristics. Thus, the dictionary which is adaptive to local structures is needed. Considering the highly structured information of saliency and edge regions, we present a novel sparse representation based super resolution approach. Salient regions are segmented to train the saliency dictionary. The same is true for edge regions. Thus, more adaptive dictionaries are acquired. When reconstructing the input image, dictionaries are chosen adaptively and then more clear details are achieved. Objective quality evaluation shows that our proposed algorithm achieves highest PSNR results comparing with the state-of-the-art methods. And subjective results demonstrate the proposed method reduces artifacts and preserves more details.