Enhanced Context Attention Network for Image Super Resolution

Abstract

The performance of image super-resolution (SR) have been greatly improved with deep convolution neural network (CNN). Despite image SR targets at recovering high-frequency details, most SR methods still focus on generating high-level features via a deep and wide network. They lack the discriminative ability of high-frequency information hidden in the abundant CNN features, thus hindering CNNs to yield better SR results. To tackle this issue, we propose two new attention mechanism: context weighted channel attention (CWCA) and persistent spatial attention (PSA). They modulate abundant features by suppressing the useless features and enhancing the interested ones in a channel-and-spatial manner. The network is then enabled to concentrate more on informative features closely related to the high-frequency components of an image. Furthermore, we propose enhanced attention residual groups with dense connection (EARG-D) to capture not only short-term information but also long-term information to maintain more useful features. Finally, we construct a deep enhanced context attention super resolution network (EASR) for better image reconstruction. Quantitative and qualitative experiments well demonstrate that our proposed method performs better than existing state-of-the-art SR methods.