Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness and gradient phase angle

This paper introduces an algorithm for single-image super-resolution based on selective sparse representation over a set of low- and high-resolution cluster dictionary pairs. Patch clustering in the dictionary training stage and model selection in the reconstruction stage are based on patch sharpness and orientation defined via the magnitude and phase of the gradient operator. For each cluster, a pair of coupled low- and high- resolution dictionaries is learned. In the reconstruction stage, the most appropriate dictionary pair is selected for the low- resolution patch and the sparse coding coefficients with respect to the low- resolution dictionary are calculated. A high-resolution patch estimate is obtained by multiplying the sparse coding coefficients with the corresponding high-resolution dictionary. The performance of the proposed algorithm is tested over a set of natural images. Results validated in terms of PSNR, SSIM and visual comparison indicate that the proposed algorithm is competitive with the state-of-the-art super-resolution algorithms.