Image super-resolution by structural sparse coding

Sparsity-based super-resolution has attracted lots of attention. Due to the high dimensionality of image data, sparsity-based methods are often in a patch-wise manner and simply impose the smoothness constraints on the overlapped regions between reconstructed patches. However, the imposed smoothness constraint is commonly weak to regularize super-resolution problem when the observed low-resolution image loses structure information. In this paper, we propose to improve the performance of the sparsity-based method by incorporating the structural correlations between neighboring patches. Concretely, the structural information is contained by the dictionary atoms which are used to sparsely represent the image patches. Incorporating the correlations of dictionary atoms into the basic sparse coding, a structural sparse coding algorithm is proposed. Experimental results demonstrate that the proposed algorithm outperforms the sparsity-based baseline in both objective and subjective quality.