Single Image Super-Resolution Based on Non-Subsampled Shearlet Transform

Abstract

With the development of deep learning, breakthroughs in single image super-resolution have been achieved. However, most existing methods are limited to using only spatial domain information or only frequency domain information, and the rich information of the image in the frequency domain space is not fully utilized, so it is still difficult to recover satisfactory texture details. In this paper, we propose a method to fuse the frequency domain and spatial domain information. Our method uses a two-branch network to extract the spatial domain information and the frequency domain information separately and uses a fusion module to fuse the different information in the two domains. We also use the Non-Subsampled Shearlet Transform (NSST) to preserve the texture directionality well, and design two NSST-based directional texture enhancement modules, which are embedded in different parts of the network, to enhance the recovery of texture details in the image reconstruction process. Quantitative and qualitative experimental results show that the method outperforms advanced single-image super-resolution methods in recovering images.