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18 May 2020 Recursive modified dense network for single-image deraining
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Blurred images caused by rain streaks can degrade the performance of many computer vision algorithms. Therefore, the single-image rain removal problem has attracted tremendous interest. Although deep learning-based deraining methods have made significant progress, there are still many issues to be addressed in terms of improving the performance. We propose a recursive modified dense network for single-image deraining. As rain streaks have different sizes and shapes, contextual information is very important for rain removal. We use a dense network to extract image features and modify the network by removing all batch normalization layers. A simple deep network cannot completely remove rain streaks from the image, while increasing the network depth will make the computing more complicated. We take a dense block with loops to remove rain streaks stage by stage. Extensive experiments on both synthetic and real-world datasets show that the proposed method can achieve competitive results in comparison with the state-of-the-art methods for single-image rain removal.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00© 2020 SPIE and IS&T
Guoqiang Chai, Zhaoba Wang, Guodong Guo, Youxing Chen, Yong Jin, Wei Wang, and Xia Zhao "Recursive modified dense network for single-image deraining," Journal of Electronic Imaging 29(3), 033006 (18 May 2020).
Received: 2 November 2019; Accepted: 7 May 2020; Published: 18 May 2020

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