Papers › Reversible Decoupling Network for Single Image Reflection Removal
Reversible Decoupling Network for Single Image Reflection Removal
Hao Zhao, Mingjia Li, Qiming Hu, Xiaojie Guo
Recent deep-learning-based approaches to single-image reflection removal have shown promising advances, primarily for two reasons: 1) the utilization of recognition-pretrained features as inputs, and 2) the design of dual-stream interaction networks. However, according to the Information Bottleneck principle, high-level semantic clues tend to be compressed or discarded during layer-by-layer propagation. Additionally, interactions in dual-stream networks follow a fixed pattern across different layers, limiting overall performance. To address these limitations, we propose a novel architecture called Reversible Decoupling Network (RDNet), which employs a reversible encoder to secure valuable information while flexibly decoupling transmission- and reflection-relevant features during the forward pass. Furthermore, we customize a transmission-rate-aware prompt generator to dynamically calibrate features, further boosting performance. Extensive experiments demonstrate the superiority of RDNet over existing SOTA methods on five widely-adopted benchmark datasets. RDNet achieves the best performance in the NTIRE 2025 Single Image Reflection Removal in the Wild Challenge in both fidelity and perceptual comparison. Our code is available at https://github.com/lime-j/RDNet
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Reflection Removal | Nature | RDNet | PSNR | 26.21 | #2 of 5 | Archive leaderboard | report |
| Reflection Removal | Nature | RDNet | SSIM | 0.842 | #2 of 5 | Archive leaderboard | report |
| Reflection Removal | Nature | Zhu et al. | PSNR | 26.04 | #3 of 5 | Archive leaderboard | report |
| Reflection Removal | Nature | Zhu et al. | SSIM | 0.846 | #3 of 5 | Archive leaderboard | report |
| Reflection Removal | Real20 | RDNet | PSNR | 25.58 | #1 of 8 | Archive leaderboard | report |
| Reflection Removal | Real20 | RDNet | SSIM | 0.846 | #1 of 8 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Objects) | RDNet | PSNR | 26.78 | #1 of 7 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Objects) | RDNet | SSIM | 0.921 | #1 of 7 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Objects) | Zhu et al. | SSIM | 0.931 | #7 of 7 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Postcard) | RDNet | PSNR | 26.33 | #1 of 6 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Postcard) | RDNet | SSIM | 0.922 | #1 of 6 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Wild) | RDNet | PSNR | 27.7 | #1 of 6 | Archive leaderboard | report |
| Reflection Removal | SIR^2(Wild) | RDNet | SSIM | 0.915 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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