Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network

• URL: http://arxiv.org/abs/2001.08388v3
• Date: Wed, 7 Apr 2021 08:27:50 GMT
• Title: Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
• Authors: Yanyan Wei, Zhao Zhang, Yang Wang, Haijun Zhang, Mingbo Zhao, Mingliang Xu, Meng Wang
• Abstract summary: We propose a new semi-supervised GAN-based deraining network termed Semi-DerainGAN. It can use both synthetic and real rainy images in a uniform network using two supervised and unsupervised processes. To deliver better deraining results, we design a paired discriminator for distinguishing the real pairs from fake pairs.
• Score: 45.78251508028359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Removing the rain streaks from single image is still a challenging task, since the shapes and directions of rain streaks in the synthetic datasets are very different from real images. Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i.e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results. In this paper, we propose a new semi-supervised GAN-based deraining network termed Semi-DerainGAN, which can use both synthetic and real rainy images in a uniform network using two supervised and unsupervised processes. Specifically, a semi-supervised rain streak learner termed SSRML sharing the same parameters of both processes is derived, which makes the real images contribute more rain streak information. To deliver better deraining results, we design a paired discriminator for distinguishing the real pairs from fake pairs. Note that we also contribute a new real-world rainy image dataset Real200 to alleviate the difference between the synthetic and real image do-mains. Extensive results on public datasets show that our model can obtain competitive performance, especially on real images.

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