Super Resolution of Single Image Based on Multi Level Residual Self Attention Mechanism

The existing network models achieve good reconstruction effect by deepening the network depth, but most of them have problems such as insufficient feature information extraction, single scale of feature information, weak perception of valuable information and so on. In order to solve this problem, this paper proposes a single image super-resolution network based on multi-level residual self attention mechanism. Firstly, shallow features and deep features are extracted from the input low resolution image hierarchically, and then convolution operation is performed on the deep features and shallow features to obtain high resolution image. Compared with the existing comparison methods, the reconstruction effect of this method is better, and the objective evaluation indexes PSNR and SSIM are also improved.