Selective super-resolution via sparse representations of sharp image patches using multiple dictionaries and bicubic interpolation

Abstract

This paper proposes an extension to the algorithm of single-image super-resolution based on selective sparse representation over a set of coupled low and high resolution dictionary pairs. The extended algorithm reserves the sparse representation framework for patches of high sharpness values while bicubic interpolation is used to super-resolve un-sharp patches. A set of cluster dictionary pairs is used for the super-resolution process. If a patch belong to a low sharpness cluster, it is super-resolved using bicubic interpolation. Otherwise, the this patch is sparsely coded over the cluster's low resolution dictionary. Then, the sparse coding coefficients of the low resolution patch along with the cluster's high resolution patch are used to estimate the corresponding high resolution patch. It is found empirically that a large percentage of patches have low sharpness values. Therefore, the usage of bicubic interpolation significantly reduces the super-resolution computational complexity, without sacrificing the reconstruction quality. Experimental results conducted over several images validate this result in terms of the PSNR and SSIM measures.