SINGLE IMAGE SUPER-RESOLUTION USING SPARSE REPRESENTATIONS WITH STRUCTURE CONSTRAINTS

This paper describes a new single-image super-resolution algorithm based on sparse representations with image structure constraints. A structure tensor based regularization is introduced in the sparse ap-proximation in order to improve the sharpness of edges. The new formulation allows reducing the ringing artefacts which can be ob-served around edges reconstructed by existing methods. The pro-posed method, named Sharper Edges based Adaptive Sparse Domain Selection (SE-ASDS), achieves much better results than many state-of-the-art algorithms, showing significant improvements in terms of PSNR (average of 29.63, previously 29.19), SSIM (average of 0.8559, previously 0.8471) and visual quality perception