Mask-DerainGAN: Learning to remove rain streaks by learning to generate rainy images

Abstract

Image deraining with unpaired data has been a challenging problem. Previous methods suffer from either the color distortion artifacts, due to the pixel-level cycle consistency loss, or the time-consuming training process. To address these problems, in this paper, we propose a novel method for rain removal based on using unpaired data. First, we obtain a rain streak mask from the derained result, which serves as a guidance for generating rainy images. Both the mask and the rain-free image are then fed into the proposed generator to obtain a high-quality rainy image, which implicitly helps improve the rain removal performance. In this way, the proposed learning framework simultaneously learns rain removal and rain generation in order to produce high-quality rain-free images and rainy images. Second, we propose a contrastive learning generator to preserve background texture details and ensure semantic consistency between the generated rain-free image and the original input. Experimental results demonstrate that our method surpasses most state-of-the-art unsupervised methods on multiple benchmark synthetic and real datasets.