Image Super-Resolution via Sparse Representation over Coupled Dictionary Learning Based on Patch Sharpness

In this paper a new algorithm for single-image super-resolution based on sparse representation over a set of coupled low and high resolution dictionary pairs is proposed. The sharpness measure is defined via the magnitude of the gradient operator and is shown to be approximately scale-invariant for low and high resolution patch pairs. It is employed for clustering low and high resolution patches in the training stage and for model selection in the reconstruction stage. A pair of low and high resolution dictionaries is learned for each cluster. The sharpness measure of a low resolution patch is used to select the appropriate cluster dictionary pair for reconstructing the high resolution counterpart. The sparse representation coefficients of low and high resolution patches are assumed to be equal. By multiplying the high resolution dictionary and the sparse coding coefficient of a low resolution patch, the corresponding high resolution patch is reconstructed. Simulation results in terms of PSNR and SSIM and visual comparison, indicate the superior performance of the proposed algorithm compared to the leading super-resolution algorithms in the literature over a set of natural images in sharp edges and corners.