Single image super resolution based on multi-scale structural self-similarity

Multi-scale structural self-similarity refers to those similar structures either within the same scale or across diffrent scales coming from the same image, which widely occur in remote sensing images. In this paper, we propose a single image super resolution (SR) method based on multi-scale structural self-similarity, which combines compressive sensing framework and structural self-similarity. In our method, the nonlocal and the pyramid-based K-SVD methods are used to add the extra information hidden in multi-scale structural self-similarity into the reconstructed image in the compressive sensing framework. The advantage of our method is that it only uses a single low-resolution image to promote spatial resolution by fully exploiting the extra information hidden in the image itself. Experimental results demonstrate that our method can improve spatial resolution more effctively compared with the CSSS and the ASDSAR methods.