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

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.