The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Joint Bi-layer Optimization for Single-Image Rain Streak Removal
Abstract
We present a novel method for removing rain streaks from a single input image by decomposing it into a rain-free background layer B and a rain-streak layer R. A joint optimization process is used that alternates between removing rain-streak details from B and removing non-streak details from R. The process is assisted by three novel image priors. Observing that rain streaks typically span a narrow range of directions, we first analyze the local gradient statistics in the rain image to identify image regions that are dominated by rain streaks. From these regions, we estimate the dominant rain streak direction and extract a collection of rain-dominated patches. Next, we define two priors on the background layer B, one based on a centralized sparse representation and another based on the estimated rain direction. A third prior is defined on the rain-streak layer R, based on similarity of patches to the extracted rain patches. Both visual and quantitative comparisons demonstrate that our method outperforms the state-of-the-art.
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Cites
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Removing Rain from Single Images via a Deep Detail Network
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Deep Joint Rain Detection and Removal from a Single Image
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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
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
Li, X., Wu, J., Lin, Z., Liu, H., & Zha, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_16 · Cited by 927
Semi-Supervised Transfer Learning for Image Rain Removal
Wei, W., Meng, D., Zhao, Q., Xu, Z., & Wu, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00400 · Cited by 458
Xiao, J., Fu, X., Liu, A., Wu, F., & Zha, Z.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183612 · Cited by 389
All in One Bad Weather Removal Using Architectural Search
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Metadata
- Authors
- Zhu, L., Fu, C.-W., Lischinski, D., & Heng, P.-A.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2545-2553
- OpenAlex
- W2777170053
- Cited by
- 334
- References
- 36
- FWCI
- 8.49
- Type
- conference paper
- Language
- en
- Open access
- closed
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