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.