Content-guided deep residual network for single image super-resolution

Abstract

We propose a content-guided deep residual network for single image super-resolution. Firstly, the relationship between the sparsity of images and the difficulty of reconstruction through a convolution network is studied. On this basis, a guided residual block is built, which can compensate for part of the high-frequency information and improve the convergence of the network. On the other hand, local loss and curvature loss values are integrated into the loss function, enhancing the network's ability to perceive the advanced texture information of the image. Finally, the optimized loss function is used to the network composed of several guided residual blocks. Experimental results demonstrate that the network achieves superior results over some existing deep residual networks in PSNR and SSIM evaluation.