Momentum feature comparison network based on generative adversarial network for single image super-resolution

Abstract

Most super-resolution methods are trained on datasets where high-resolution images and corresponding low-resolution images are obtained by the fixed degradation method. However, these external-based methods would fail to recover the detailed information of the test images if it could not be found in datasets. In this paper, we propose the momentum feature comparison network to generate the super-resolution image with rich texture information. Without the participation of high-resolution images in the training process, our method belongs to the completely unsupervised super-resolution method. A siamese structure with momentum update in generator is proposed to expand the content information of the low-resolution image and maintains the continuous consistency of intermediate feature maps. Furthermore, the results of two branches are fused through the feature fusion module to retain the global distribution of features and enhance local high-frequency details. The experimental results show that our method achieves great results compared with the state-of-the-art methods.