Single-Image Super-Resolution Based on Decomposition and Sparse Representation

In this paper, we propose a novel method for solving single-image super-resolution problems. Firstly, using the human visual perception and image gradient features, the image total variation is decomposed into structural components and texture components. Based on the theory about sparse signal representation, we used K-SVD method to generate ultra-complete dictionary and to achieve the reconstruction of the texture component. Then the super-resolution reconstruction of the whole original low resolution image is realized by fused them with the bi-cubic interpolated image reconstruction of the structural components. The proposed method, without external image database support, brings in the whole image information while depends on the fixed -K neighborhood. It can upgrade the fitting performance of the existing methods, and enhance mole detail image information, also improve the reconstructed image quality.