Deep recurrent residual channel attention network for single image super-resolution.

The models based on convolutional neural network have achieved excellent results in image super-resolution by acquiring prior knowledge from a large number of images, but such models still have problems such as the features between layers in the depth network cannot be effectively fused, the number of parameters is too large, and cross-channel feature learning is impossible. Based on this, a deep recursive residual channel attention network (DRRCAN) model was proposed in this paper. To solve the problem that the information between different layers in the deep network cannot be fused effectively, this paper constructs a channel feature fusion module, which can effectively fuse the feature information of different layers. To solve the problem that the parameters increase sharply due to the increase of network depth, recursive blocks are adopted in this paper, which greatly reduces the number of parameters in the deep network. The channel attention is integrated to enable the model to learn features across channels. In addition, to avoid gradient explosion or disappearance, residual modules, long skip connections are introduced to improve the stability and generalization ability of the model. Extensive benchmark evaluations validate the superiority of the proposed DRRCAN model compared with existing algorithms. [ABSTRACT FROM AUTHOR]