Image Super-Resolution based on the Dual-Dictionary Learning and Sparse Representation Method

Learning based image super-resolution method is used to reconstruct high-frequency (HF) details from the prior model trained by a set of high-(HR) and low-resolution (LR) image patches. In this paper, newly proposed novel image superresolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively.The experimental results on test images validate that by employing the proposed method, more image details can be recovered and much better results can be achieved than the stateof-the-art algorithms in terms of both PSNR and visual perception. Keywords—Learning-based, High resolution, Low Resolution, RHF.