Context-Aware Residual Network with Promotion Gates for Single Image Super-Resolution

Abstract

Deep learning models have achieved significant success in quantities of vision-based applications. However, directly applying deep structures to perform single image super-resolution (SISR) results in poor visual effects such as blurry patches and loss in details, which are caused by the fact that low-frequency information is treated equally and ambiguously across different patches and channels. To ease this problem, we propose a novel context-aware deep residual network with promotion gates, named as G-CASR network, for SISR. In the proposed G-CASR network, a sequence of G-CASR modules is cascaded to transform low-resolution features to high informative features. In each G-CASR module, we also design a dual-attention residual block (DRB) to capture abundant and variant context information by dually connecting spatial and channel attention scheme. To improve the informative ability of extracted context information, a promotion gate (PG) is further applied to analyze inherent characteristics of input data at each module, thus offering insight for how to enhance contributive information and suppress useless information. Experiments on five public datasets consisting of Set5, Set14, B100, Urban100 and Manga109 show that the proposed G-CASR has achieved averagely 1.112/0.0255 improvement for PSNR/SSIM measurements comparing with the recent methods including SRCNN, VDSR, lapSRN and EDSR. Simultaneously, the proposed G-CASR requires only about 25% memory cost comparing with EDSR.