关键词: 超分辨率, 高频残差字典, 正交匹配追踪算法, 迭代反投影

Abstract: The fast single image super-resolution via self-example learning and sparse representation makes the artifacts of reconstructed image obvious and the reconstruction efficiency low. This paper proposes a fast image super-resolution algorithm for residual dictionary learning to eliminate the artifacts and improve the reconstruction speed. The dictionary is trained by using a high frequency residual map based on an external image set, which reduces the complexity of dictionary training, enhances the representation ability of high-frequency information and eliminates the reconstructed artifacts. To solve the problem of high complexity of sparse representation, the orthogonal matching pursuit algorithm based on Cholesky decomposition is used to quickly obtain the sparse coefficients, and the high frequency residual dictionary is used to realize super-resolution reconstruction. The use of iterative back-projection further improves the reconstructed image quality, which greatly improves the efficiency of the algorithm and achieves better reconstructed image. The experiment results show that the proposed algorithm improves the peak signal to noise ratio and visual effect. Compared with the traditional algorithm, the proposed algorithm runs faster and gets the enhanced texture and better quality of reconstructed images.

Key words: super-resolution, high frequency residual dictionary, orthogonal matching pursuit algorithm, iterative back-projection