Channel Hourglass Residual Network For Single Image Super-Resolution

Abstract

Deep convolutional neural networks (CNNs) for Super-Resolution (SR) from low-resolution (LR) images have achieved remarkable reconstruction performance with the utilization of residual networks and visual attention mechanism. However, the existing single image super-resolution (SISR) methods with deeper or wider network architectures encounter module representation bottleneck and neglect module efficiency in real-world applications. To solve these issues, in this paper, we design channel hourglass residual structure (CHRS) consisted of several nested residual modules for reducing parameters and extracting more representational features. Furthermore, we integrate channel attention (CA) mechanism into CHRS to generate channel hourglass residual block (CHRB) which can be easily extended to other methods for improving performance. We also propose channel hourglass residual network (CHRN) which not only pays attention to network learning efficiency but also learns more discriminative expressions. Extensive experiments demonstrate the effectiveness of our CHRN and the generalization ability of our CHRB.