Dual-view Attention Networks for Single Image Super-Resolution

One non-negligible flaw of the convolutional neural networks (CNNs) based single image super-resolution (SISR) models is that most of them are not able to restore high-resolution (HR) images containing sufficient high-frequency information. Worse still, as the depth of CNNs increases, the training easily suffers from the vanishing gradients. These problems hinder the effectiveness of CNNs in SISR. In this paper, we propose the Dual-view Attention Networks to alleviate these problems for SISR. Specifically, we propose the local aware (LA) and global aware (GA) attentions to deal with LR features in unequal manners, which can highlight the high-frequency components and discriminate each feature from LR images in the local and global views, respectively. Furthermore, the local attentive residual-dense (LARD) block that combines the LA attention with multiple residual and dense connections is proposed to fit a deeper yet easy to train architecture. The experimental results verified the effectiveness of our model compared with other state-of-the-art methods.