Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal

Yiyang Shen, Yongzhen Wang, Mingqiang Wei, Honghua Chen, Haoran Xie, Gary Cheng, Fu Lee Wang

Research output: Contribution to journalArticlepeer-review


Real-world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN). Unlike most of existing methods, Semi-MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation; and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real-world rainy and clean images to bridge the gap between synthetic and real-world rain. Extensive experiments show clear improvements of our approach over twenty representative state-of-the-arts on both synthetic and real-world rainy images. Source code is available at

Original languageEnglish
Pages (from-to)443-454
Number of pages12
JournalComputer Graphics Forum
Issue number7
Publication statusPublished - Oct 2022


  • CCS Concepts
  • • Computing methodologies → Image Processing


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