An unsupervised generative adversarial network for single image deraining

Abstract

As the basis of image processing, single image deraining has always been a significant and challenging issue. Due to the lack of real rainy images and corresponding clean images, most deraining networks are trained by synthetic datasets, which makes the output images unsatisfactory in real applications. Besides, note that a heavy rainfall is typically accompanied with some fog. Although some deraining networks have been proposed to remove the rain streaks in the rainy images, the output images may still be blurred due to the accompanied fog. In this paper, these problems existing in single image deraining is comprehensively considered, and propose a Cycle-Derain network based on an unsupervised attention-guided mechanism. Specifically, the Cycle-Derain network takes advantage of generative adversarial networks with two mappings and the cycle consistency loss to train both unpaired rainy images and rain-free images. Moreover, it introduces an unsupervised attention-guided mechanism and exploits the loop-search positioning algorithm to deal with the details of rain and fog in images. Extensive experiments have been carried out, and the results show that the proposed Cycle-Derain network is preferable compared with other deraining networks, especially in term of rainy image restoration.