The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Removing Rain from Single Images via a Deep Detail Network
Abstract
We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.
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Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Identity mappings in deep residual networks
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“The base layer can be obtained using low-pass filtering of X [11, 32, 37] after which the detail layer Xdetail = X−Xbase.”
MethodAccurate Image Super-Resolution Using Very Deep Convolutional Networks
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Cited by
Pre-Trained Image Processing Transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01212 · Cited by 2,308
Multi-Stage Progressive Image Restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., & Shao, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01458 · Cited by 2,095
Density-Aware Single Image De-raining Using a Multi-stream Dense Network
Zhang, H., & Patel, V. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00079 · Cited by 1,066
MAXIM: Multi-Axis MLP for Image Processing
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00568 · Cited by 616
Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network
Guo, Y., Li, H., & Zhuang, P. (2020) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2019.2911447 · Cited by 508
Metadata
- Authors
- Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., & Paisley, J.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1715-1723
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2740982616
- Cited by
- 1,235
- References
- 12
- FWCI
- 25.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., & Paisley, J. (2017). Removing Rain from Single Images via a Deep Detail Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.186
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