Single image super resolution based on sparse representation via directionally structured dictionaries

This paper introduces a single-image super-resolution algorithm based on selective sparse coding over several directionally structured learned dictionaries. The sparse coding of high-resolution (HR) image patch over a HR dictionary is assumed to be identical to that of the corresponding low-resolution (LR) patches as coded over a coupled LR dictionary. However, the training patches are clustered by measuring the similarity between a patch and a number of directional templates sets. Each template set characterizes directional variations possessing a specific directional structure. For each cluster, a pair of directionally structured dictionaries is learned; one dictionary for each resolution level. An analogous clustering is performed in the reconstruction phase; each LR image patch is decided to belong to a specific cluster based on its directional structure. This decision allows for selective sparse coding of image patches, with improved representation quality and reduced computational complexity [1]. With appropriate sparse model selection, the proposed algorithm is shown to out-perform a leading super-resolution algorithm which uses a pair of universal dictionaries. Simulations validate this result both visually and quantitatively, with an average of 0.2 dB improvement in PSNR over Kodak set and some benchmark images.