Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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.
Description
CCS Concepts: Computing methodologies → Image Processing
@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}
}