Research map / 3D vision / Depth and stereo
Depth-Attentional Features for Single-Image Rain Removal
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use depth-attentional features instead of removing the top decoder branch and DAF (ablation) | DAF-Net · RainCityscapes test set | PSNR: 28.56 -> 30.06 | Helped |
| use depth-attentional features instead of removing the top decoder branch and DAF (ablation) | DAF-Net · RainCityscapes test set | SSIM: 0.9457 -> 0.9530 | Helped |
| set the number of attention weights to 64 rather than 16, 32, or 128 (ablation) | DAF-Net · RainCityscapes test set | PSNR: 29.90 (n=16), 29.94 (n=32), 30.06 (n=64), 29.93 (n=128) | Helped |
| set the number of attention weights to 64 rather than 16, 32, or 128 (ablation) | DAF-Net · RainCityscapes test set | SSIM: 0.9527 (n=16), 0.9528 (n=32), 0.9530 (n=64), 0.9524 (n=128) | Helped |
Closest papers by meaning
- Deep Joint Rain Detection and Removal From a Single Image (CVPR 2017 · similarity 0.92)
- Removing Rain From Single Images via a Deep Detail Network (CVPR 2017 · similarity 0.92)
- Singe Image Rain Removal with Unpaired Information: A Differentiable Programming Perspective (AAAI 2019 · similarity 0.90)
- Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains (ICLR 2024 · similarity 0.89)
- Boosting Image De-Raining via Central-Surrounding Synergistic Convolution (AAAI 2025 · similarity 0.88)
- Towards Universal Rainy Image Restoration: Benchmark and Baseline (AAAI 2025 · similarity 0.87)
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