To solve the problem of insufficient sample resources and poor noise immunity for single image Super Resolution (SR) restoration, a single image super-resolution algorithm based on structural self-similarity and deformation block feature was proposed. Firstly, a scale model was constructed to expand search space as much as possible and overcome the shortcomings of lack of a single image super-resolution training sample. Secondly, the limited internal dictionary size was increased by geometric deformation of sample block. Finally, in order to improve anti-noise performance of reconstructed picture, the group sparse learning dictionary was used to reconstruct image. The experimental results show that compared with the excellent algorithms such as Bicubic, Sparse coding Super Resolution (ScSR) algorithm and Super-Resolution Convolutional Neural Network (SRCNN) algorithm, the super-resolution images with more subjective visual effects and higher objective evaluation can be obtained, the Peak Signal-To-Noise Ratio (PSNR) of the proposed algorithm is increased by about 0.35 dB on average. In addition, the scale of dictionary is expanded and the accuracy of search is increased by means of geometric deformation, and the time consumption of algorithm is averagely reduced by about 80 s.

Aiming at the ill-posed inverse problem of single-image Super Resolution (SR) restoration, a single image super resolution algorithm combining with structural self-similarity and convolution networks was proposed. Firstly, the self-structure similarity of samples to be reconstructed was obtained by scaling decomposition. Combined with external database samples as training samples, the problem of over-dispersion of samples could be solved. Secondly, the sample was input into a Convolution Neural Network (CNN) for training and learning, and the prior knowledge of the super resolution of the single image was obtained. Then, the optimal dictionary was used to reconstruct the image by using a nonlocal constraint. Finally, an iterative backprojection algorithm was used to further improve the image super resolution effect. The experimental results show that compared with the excellent algorithms such as Bicubic, K-SVD (Singular Value Decomposition of k iterations) algorithm and Super-Resolution Convolution Neural Network (SRCNN) algorithm, the proposed algorithm can get super-resolution reconstruction with clearer edges.