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DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2021-04-30 , DOI: 10.1109/tip.2021.3074804
Yanyan Wei, Zhao Zhang, Yang Wang, Mingliang Xu, Yi Yang, Shuicheng Yan, Meng Wang

Single Image Deraining (SID) is a relatively new and still challenging topic in emerging vision applications, and most of the recently emerged deraining methods use the supervised manner depending on the ground-truth (i.e., using paired data). However, in practice it is rather common to encounter unpaired images in real deraining task. In such cases, how to remove the rain streaks in an unsupervised way will be a challenging task due to lack of constraints between images and hence suffering from low-quality restoration results. In this paper, we therefore explore the unsupervised SID issue using unpaired data, and propose a new unsupervised framework termed DerainCycleGAN for single image rain removal and generation, which can fully utilize the constrained transfer learning ability and circulatory structures of CycleGAN. In addition, we design an unsupervised rain attentive detector (UARD) for enhancing the rain information detection by paying attention to both rainy and rain-free images. Besides, we also contribute a new synthetic way of generating the rain streak information, which is different from the previous ones. Specifically, since the generated rain streaks have diverse shapes and directions, existing derianing methods trained on the generated rainy image by this way can perform much better for processing real rainy images. Extensive experimental results on synthetic and real datasets show that our DerainCycleGAN is superior to current unsupervised and semi-supervised methods, and is also highly competitive to the fully-supervised ones.

中文翻译:

DerainCycleGAN:专注于雨水的CycleGAN,用于单图像排水和制雨

在新兴的视觉应用中,单图像消除(SID)是一个相对较新且仍具有挑战性的主题,并且最近出现的大多数消除方法都根据地面真实性(即,使用配对数据)使用监督方式。然而,实际上在实际的排练任务中遇到未配对的图像是相当普遍的。在这种情况下,由于图像之间缺乏约束,因此如何以无人看管的方式去除雨条纹将是一项艰巨的任务,因此会遭受低质量的恢复结果的困扰。因此,在本文中,我们将使用不成对的数据探索无监督的SID问题,并提出一个称为DerainCycleGAN的新的无监督框架,用于单图像雨水的去除和生成,该框架可以充分利用CycleGAN的受限转移学习能力和循环结构。此外,我们设计了一种无监督的降雨注意检测器(UARD),通过同时注意多雨和无雨图像来增强降雨信息检测。此外,我们还提供了一种新的综合方法来生成降雨条信息,这与以前的方法不同。具体地,由于生成的雨条纹具有不同的形状和方向,因此通过这种方式在生成的雨图像上训练的现有的去杂边方法可以更好地处理真实的雨图像。在综合和真实数据集上的大量实验结果表明,我们的DerainCycleGAN优于当前的非监督和半监督方法,并且与完全监督的方法相比具有很高的竞争力。
更新日期:2021-05-11
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