From coarse to fine: A two stage conditional generative adversarial network for single image rain removal

Images captured in rainy days are often obscured by rain streaks which affect the accuracy of object detection, vehicle and pedestrian recognition. It is hard to restore the texture and color information of the de-rained image by some conventional rain removal algorithms. In order to address the problem, we propose a novel two stage conditional generative adversarial network (TS-CGAN), in which the generator network of the TS-CGAN contains two stage frameworks to better gradually remove rain streaks. In addition, compared with previous neural networks on the rain removal work, patch-GAN discriminator of the TS-CGAN can able to encourage generator to adversely produce satisfactory visual clean images that can solve the artifact problem. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method significantly outperforms recent state-of-the-art algorithms in terms of qualitative and quantitative measurement. (C) 2021 Elsevier Inc. All rights reserved.