RSAMSR: A deep neural network based on residual self-encoding and attention mechanism for image super-resolution

This paper proposes an improved residual self-encoding and attention mechanism super-resolution (RSAMSR) network. Firstly, we construct a new structure through the multi-path convolution and design an attention mechanism module. Then the input data are divided into high and low-frequency components sent to the residual network with different depths for processing based on the spatial scaling theory. Finally, we introduce a self-encoding network to remove image noise. The model uses the L1 loss function for data training on the DIV2K data set and is compared with some state-of-the-art SR networks in four different public datasets of Set5, Set14, B100, and Urban100 under the magnification factor x2, x3, and x4. Detailed experimental results show that the proposed model has fewer model parameters, the best objective criteria PSNR and SSIM, and the best subjective visual effect.