Single image super-resolution reconstruction based on split-attention networks

A single image super-resolution reconstruction method for splitting attention networks is proposed to address the problems of lack of texture details, insufficient feature extraction, and unstable training in the existing generation of adversarial networks under large-scale factors. Firstly, the generator is constructed using the split attention residual module as the basic residual block, which improves the generator's feature extraction ability. Secondly, Charbonnier loss function with better robustness and focal frequency loss are introduced into the loss function to replace the mean square error loss function, and regularization loss smoothing training results are added to prevent the image from being too pixelated.Finally, spectral normalization is used in both the generator and discriminator to improve the stability of the network. Compared with other methods tested on Set5, Set14, Urban100 and BSDS100 test sets at a magnification factor of 4, the peak signal-to-noise ratio of this method is 1.419 dB higher than the average value of other comparison methods in this article, and the structural similarity is 0.051 higher than the average value. Experimental data and renderings indicate that this method subjectively has rich details and better visual effects, while objectively has high peak signal-to-noise ratio and structural similarity values.