Image super-resolution based on residually dense distilled attention network q

Deep convolutional neural networks (CNNs) have been playing an increasingly important role in image super-resolution (SR). However, if we just deepen or widen the networks, it could result in the excess of parameters and the increase of training difficulty. In this paper, we propose a residually dense distilled attention network (RDDAN) to address the problems in SR. Residual networks could make full use of the information of previous layers. In RDDAN we propose a connection block group (CBG), which is stacked in the feature extraction module of the network. CBG consists of two parts, dense enhancement network (DEN) and channel attention producing (CAP) module. First, instead of simply stacking residual blocks, DEN utilizes feature distillation with both dense concatenation and skip connection to extract deep and shallow features, which could enhance the representation ability. Second, with attention mechanism, CAP pays attention to the channel-wise association to adjust channel-wise features and restore high frequency feature information. By evaluating the performance of results based on benchmark methods, our method achieves a more desirable performance than state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.