Coupled Rain Streak and Background Estimation via Separable Element-wise Attention
Single image de-raining is challenging especially in the scenarios with dense rain streaks. Existing methods resolve this problem by predicting the rain streaks of the image, which constrains the network to focus on local rain streaks features. However, dense rain streaks are visually similar to mis...
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| Main Authors: | , , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2020
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/8368 https://ink.library.smu.edu.sg/context/sis_research/article/9371/viewcontent/Coupled_rain_streak_and_background_estimation_via_separable_element_wise_attention.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Single image de-raining is challenging especially in the scenarios with dense rain streaks. Existing methods resolve this problem by predicting the rain streaks of the image, which constrains the network to focus on local rain streaks features. However, dense rain streaks are visually similar to mist or fog (with large intensities), in this case, the training objective should be shifted to image recovery instead of extracting rain streaks. In this paper, we propose a coupled rain streak and background estimation network that explores the intrinsic relations between two tasks. In particular, our network produces task-dependent feature maps, each part of the features correspond to the estimation of rain streak and background. Furthermore, to inject element-wise attention to all the convolutional blocks for better understanding the rain streaks distribution, we propose a Separable Element-wise Attention mechanism. In this way, dense element-wise attention can be obtained by a sequence of channel and spatial attention modules, with negligible computation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts on 5 existing synthesized rain datasets and the real-world scenarios, without extra multi-scale or recurrent structure. |
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