关键词: 超分辨率, 低秩矩阵恢复, 字典学习, 稀疏表示, 邻域嵌入

Abstract:

For image super-resolution reconstruction, the over-all information of the image is usually the object of study. However, the information of intrinsic structure in the image is not fully utilized. In order to improve the performance of super-resolution reconstruction by using the information of different features, a super-resolution reconstruction algorithm based on neighborhood regression and sparse representation is proposed. Firstly, the high-resolution image is decomposed into the low-rank portion and the sparse portion according to the low rank which the image has. Secondly, the different feature dictionaries are constructed according to the corresponding training samples. The sparse portion and low-rank portion of low-resolution are reconstructed based on the corresponding feature dictionary by using neighborhood regression and sparse representation. Finally, the high-resolution image is obtained based on the reconstructed sparse portion and low-rank portion according to low rank matrix recovery theory. In the test set Set5 and Set14, the proposed algorithm compares with several classical algorithms, and the visualized and quantized results show that compared with the traditional super-resolution algorithm, the proposed algorithm has a good improvement in PSNR and SSIM.

Key words: super resolution, low rank matrix restoration, dictionary learning, sparse representation, neighbor embedding