Image Super-Resolution Method with Patch Sparse Structural Similarity Neighbor Constraint

In image super-resolution （SR） of sparse neighbor embedding to inhibit visual artifact, the over fitting phenomenon is produced frequently in the high frequency information estimation with sparse prior. Therefore, the image details of edge and texture may be lost because of over smooth. In this paper, an image SR method is pro- posed via patch sparse structural similarity neighbor constraint. Firstly, the high-low resolution patch pair＇s model is constructed and the offline coupled dictionary is learned from the model. Simultaneously, we obtained the model＇s isomorph sparse coefficient. Secondly, we choose the image patch sparse structural similarity features to perform neighbor searching in patch pair＇s model. Then, the neighbor weighted approximation, the sparse linear combination and the down-sampled appro：dmation are combined as the constraint condition to construct the SR objective function. At last, the conjugate gradient algorithm is used to solve it. Some extensive experiments using common images and grabbed images are performed. The main parameters are tested and analyzed. The simulation and actual experimental results show that the method can suppress visual artifacts and preserve image detail in- formation effectively. Meanwhile, the objective evaluation results both in PSRN and SSIM are improved slightly.