Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

  • Xia Li
  • Jianlong Wu
  • Zhouchen Lin
  • Hong LiuEmail author
  • Hongbin Zha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11211)


Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage:


Recurrent neural network Squeeze and excitation block Image deraining 



Zhouchen Lin is supported by National Basic Research Program of China (973 Program) (Grant no. 2015CB352502), National Natural Science Foundation (NSF) of China (Grant nos. 61625301 and 61731018), Qualcomm, and Microsoft Research Asia. Hong Liu is supported by National Natural Science Foundation of China (Grant nos. U1613209 and 61673030). Hongbin Zha is supported by Beijing Municipal Natural Science Foundation (Grant no. 4152006).

Supplementary material

474212_1_En_16_MOESM1_ESM.pdf (15.9 mb)
Supplementary material 1 (pdf 16269 KB)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Laboratory of Machine Perception, Shenzhen Graduate SchoolPeking UniversityBeijingChina
  2. 2.Key Laboratory of Machine Perception (MOE), School of EECSPeking UniversityBeijingChina
  3. 3.Cooperative Medianet Innovation CenterShanghai Jiao Tong UniversityShanghaiChina

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