Spatial Graph Convolutional Network for Image Super-Resolution

Abstract

Convolutional neural networks (CNNs) have recently made great progress in single image super-resolution (SISR) due to their powerful feature representation. However, most existing CNN-based SR methods mainly focus on designing deeper architectures to obtain superior representations, neglecting the global feature correlations inside networks, thus limiting the discriminative learning ability. In this paper, we propose a spatial graph convolutional network (SGCN) to amplify feature representation for high quality image rendering. Specifically, we present a residual feature refinement module (RFRM) in SGCN to encode correlations in both channel and spatial dimensions of features. A novel spatial graph attention (SGA) is deployed to model the correlations with awareness of global information for the feature enhancement. Further-more, we utilize the Gram matrix to infer the global relations in features, which adaptively measures the pixel-wise spatial attention with free parameters. Experimental results demonstrate the superiority of our SGCN over state-of-the-art SISR methods in terms of quantitative metrics and visual quality.