A self-adaptive super-resolution method based on dictionary library

In recent years, with the theory of compressed sensing being proposed and applied widely, the sparse representation method has become one of the hotspots to handle the superresolution problem. Usually, this kind of algorithms use only one dictionary pair for all low-resolution patches, which makes the recovered results less satisfied due to its bad adaptability. To overcome such problem, in this paper, we propose a self-adaptive image super-resolution reconstruction algorithm in which different dictionary pairs are trained and used for recovery according to different types of low-resolution patches. All the dictionary pairs are stored in the dictionary library for reuse. We apply the proposed algorithm on human face images and generic images respectively. The results show that the algorithm is superior to the existing algorithms in reconstruction performance. Besides compared with that of generic images, the reconstruction performance of human face images is improved more greatly.