Stratified attention dense network for image super-resolution

With the development of deep learning technology, a variety of image SR approaches based on convolutional neural network (CNN) are developed to learn the mapping from low resolution (LR) to high resolution (HR). However, the feature information from raw images could not be distinguished very clearly by most of these existing methods, resulting in declining performance. In order to achieve a good image resolution, a very deep network is often used to integrate all the feature information from LR image. Obviously, deep network without fully exploring the available information achieves a very large computation complexity but cannot always ensure high image quality. To address these problems, a stratified attention dense network (SADN) is proposed in this paper to reconstruct higher quality HR images. In SADN, a stratified dense group (SDG) architecture is proposed to fully explore the feature information in LR images, including local and global information. Particularly, the attention dense module (ADM) is proposed to distinguish the extracted feature information so as to enhance the discrimination of network. The extensive experiments on benchmark datasets verify the effectiveness of the proposed method. Comparison with other state-of-the-art methods shows the superiority of the proposed SADN.