Removal of visual disruption caused by rain using cycle-consistent generative adversarial networks

Abstract

This paper addresses the problem of removing rain disruption from images for outdoor vision systems. The Cycle-Consistent Generative Adversarial Network (CycleGAN) is proposed as a more promising rain removal algorithm, as compared to the state-of-the-art Image De-raining Conditional Generative Adversarial Network (ID-CGAN). The CycleGAN has an advantage in its ability to learn the underlying relationship between the rain and rain-free domain without the need of paired domain examples. Based on rain physical properties and its various phenomena, five broad categories of real rain distortions are proposed in this paper. For a fair comparison, both networks were trained on the same set of synthesized rain-and-ground-truth image-pairs provided by the ID-CGAN work, and subsequently tested on real rain images which fall broadly under these five categories. The comparison results demonstrated that the CycleGAN is superior in removing real rain distortions.