1. Introduction
Single image super resolution (SISR) is a classic low-level computer vision task, which aims to reconstruct a super resolution (SR) image from a single low resolution (LR) image. Since SRCNN [3] was proposed, many deep learning methods [5], [10], [16], [17], [20], [23], [28], [31], [32] have been proposed to improve the performance of image super resolution. SRCNN [3] is the first attempt to reconstruct SR from LR using convolution neural network (CNN). Subsequently, Kim et al. [12] achieved better super resolution performance by increasing the network depth of SRCNN [3], which also shows that the network depth is closely related to the super resolution performance. Bee et al. [16] made greater progress in super resolution by introducing residual structures that further increased the depth of the network.