Super-resolution Using Multiple Structured Dictionaries Based on the Gradient Operator and Bicubic Interpolation

Abstract

In this paper we present an extension to the algorithm of super-resolution via selective sparse representation over a set of coupled low and high resolution cluster dictionary pairs. Patch clustering and sparse model selection are carried out using the magnitude and phase of the patch gradient operator. A compact dictionary pair is learned for each cluster. A low resolution patch is classified into one of the clusters using the two criteria. A high resolution patch is reconstructed using the high resolution cluster dictionary, and the spare representation coefficients of its low resolution counterpart over the low resolution cluster dictionary. This extension aims at super-resolving patches of low sharpness or poor directionality with bicubic interpolation. Accordingly, the computationally expensive sparse representation framework will only be applied on a limited portion of image patches. As a result, the super-resolution reconstruction computational complexity is significantly reduced without sacrificing the performance. Experiments conducted over natural images validate this result.