Iterative up and downsampling residual networks for single image super-resolution

With the development of neural networks, the effect of single-image super-resolution is getting better. However, in many models that have been proposed, the processing of the upsampling module for image magnification is relatively simple. In this paper, we propose an iterative up and downsampling residual network. We selected a baseline model, and combined the upsampling sub-module and the downsampling sub-module into the final image upsampling module in an iterative form. This module generates detailed differences of the image by connecting convolution and deconvolution, and then extracts them through the residual method to correct the texture of the upsampled image. The iterative module enhances the effect of correction. Experiments on public datasets prove the effectiveness of the model.