Unsupervised single image deraining with self-supervised constraints

Jin, Xin, Chen, Zhibo, Lin, Jianxin, Chen, Zhikai and Zhou, Wei 2019. Unsupervised single image deraining with self-supervised constraints. Presented at: 2019 IEEE International Conference on Image Processing, Taipei, Taiwan, 22-25 September 2019. 2019 IEEE International Conference on Image Processing. IEEE, pp. 2761-2765. 10.1109/ICIP.2019.8803238

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Abstract

Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality and practicality in real-world multimedia applications. Besides, due to lack of labeled-supervised constraints, directly applying existing unsupervised frameworks to the image deraining task will suffer from low-quality recovery. Therefore, we propose an Unsupervised Deraining Generative Adversarial Network (UD-GAN) to tackle above problems by introducing self-supervised constraints from the intrinsic statistics of unpaired rainy and clean images. Specifically, we design two collaboratively optimized modules, namely Rain Guidance Module (RGM) and Background Guidance Module (BGM), to take full advantage of rainy image characteristics. UD-GAN outperforms state-of-the-art methods on various benchmarking datasets in both quantitative and qualitative comparisons.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-5386-6249-6
ISSN:	1522-4880
Last Modified:	24 Aug 2023 16:00
URI:	https://orca.cardiff.ac.uk/id/eprint/161670

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