Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Date
2022Author
Shen, Yiyang
Wang, Yongzhen
Wei, Mingqiang
Chen, Honghua
Xie, Haoran
Cheng, Gary
Wang, Fu Lee
Metadata
Show full item recordAbstract
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 SEMIsupervised 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 https://github.com/syy-whu/Semi-MoreGAN.
BibTeX
@article {10.1111:cgf.14690,
journal = {Computer Graphics Forum},
title = {{Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal}},
author = {Shen, Yiyang and Wang, Yongzhen and Wei, Mingqiang and Chen, Honghua and Xie, Haoran and Cheng, Gary and Wang, Fu Lee},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14690}
}
journal = {Computer Graphics Forum},
title = {{Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal}},
author = {Shen, Yiyang and Wang, Yongzhen and Wei, Mingqiang and Chen, Honghua and Xie, Haoran and Cheng, Gary and Wang, Fu Lee},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14690}
}