Symmetrical Residual Connections for Single Image Super-Resolution

Abstract

Single-image super-resolution (SISR) methods based on convolutional neural networks (CNN) have shown great potential in the literature. However, most deep CNN models don’t have direct access to subsequent layers, seriously hindering the information flow. Furthermore, they fail to make full use of the hierarchical features from different low-level layers, thereby resulting in relatively low accuracy. In this article, we present a new SISR CNN, called SymSR, which incorporates symmetrical nested residual connections to improve both the accuracy and the execution speed. SymSR takes a larger image region for contextual spreading. It symmetrically combines multiple short paths for the forward propagation to improve the accuracy and for the backward propagation of gradient flow to accelerate the convergence speed. Extensive experiments based on open challenge datasets show the effectiveness of symmetrical residual connections. Compared with four other state-of-the-art super-resolution CNN methods, SymSR is superior in both accuracy and runtime.