Image super-resolution via nonlocal similarity and group structured sparse representation

Sparse prior provides an effective tool for the image reconstruction. However, the sparse coding for independent patches leads to the unstable sparse decomposition. In this paper, we propose a group structured sparse representation model by considering the nonlocal similarity. The nonlocal similar patches are collected and classified into groups. Patches in the same group are reconstructed based the same basis of dictionaries. The dictionary is organized as the combination of many orthogonal sub-dictionaries. To provide the redundancy, the dictionary used for the sparse coding is generated online with several sub-dictionaries, thus it is over-complete. We apply the proposed model into a gradual SR framework. The framework enlarges LR to HR by a patch enhancement and an alternative sparse reconstruction on the patch and group. Objective quality evaluation shows that our proposed SR method 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.