Abstract: The existing super-resolution reconstruction algorithms only consider the gray information of image patches, but ignores the texture information, and most nonlocal methods emphasize the nonlocal information without considering the local information. In view of these disadvantages, an image super-resolution reconstruction algorithm combined with compressed sensing and nonlocal information was proposed. Firstly, the similarity between pixels was calculated according to the structural features of image patches, and both the gray and the texture information was considered. Then, the weight of similar pixels was evaluated by merging the local and nonlocal information, and a regularization term combining the local and nonlocal information was constructed. Finally, the nonlocal information was introduced into the compressed sensing framework, and the sparse representation coefficients were solved by the iterative shrinkage algorithm. Experimental results demonstrate that the proposed algorithm outperforms other learning-based algorithms in terms of improved Peak Signal-to-Noise Ratio and Structural Similarity, and it can better recover the fine textures and effectively suppress the noise.

Key words: super-resolution reconstruction, compressed sensing, nonlocal information, sparse representation, structural feature

摘要： 针对现有的超分辨率重建算法只考虑图像块的灰度信息，而忽略了纹理信息，并且大多数非局部方法在强调非局部信息的同时，没有考虑局部信息的问题，提出一种结合压缩感知与非局部信息的图像超分辨率重建算法。首先，根据图像块的结构特征计算像素之间的相似性，同时考虑了图像块的灰度信息和纹理信息；然后，合并图像的局部和非局部信息来估计相似像素的权重，构造结合局部和非局部信息的正则项；最后，将图像的非局部信息引入到压缩感知框架中，通过迭代收缩算法求解稀疏表示系数。实验结果表明，所提算法与现有的基于学习的超分辨率算法相比，重建图像的峰值信噪比和结构相似度取值更高，并且在恢复图像纹理细节的同时有效抑制了噪声。

关键词: 超分辨率重建, 压缩感知, 非局部信息, 稀疏表示, 结构特征