Research map / Detection and segmentation / CNN features
Deep Joint Rain Detection and Removal From a Single Image
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add contextualized dilated paths rather than using one convolution path per recurrence (ablation) | JORDER · Rain12 | PSNR: 35.86 -> 36.02 | Helped |
| add contextualized dilated paths rather than using one convolution path per recurrence (ablation) | JORDER · Rain100L | PSNR: 35.41 -> 36.11 | Helped |
| add recurrent progressive rain detection and removal (ablation) | JORDER · Rain100H | PSNR: 22.15 -> 23.45 | Helped |
| add recurrent progressive rain detection and removal (ablation) | JORDER · Rain100H | SSIM: 0.6736 -> 0.7490 | Helped |
| use derain-dehaze-derain ordering instead of other tested processing orders (ablation) | JORDER-R-DEHAZE · heavy-rain and mist images | visual restoration quality | Helped |
Closest papers by meaning
- Removing Rain From Single Images via a Deep Detail Network (CVPR 2017 · similarity 0.93)
- A Model-Driven Deep Neural Network for Single Image Rain Removal (CVPR 2020 · similarity 0.93)
- Rain Streak Removal via Dual Graph Convolutional Network (AAAI 2021 · similarity 0.93)
- Removing Raindrops and Rain Streaks in One Go (CVPR 2021 · similarity 0.93)
- Boosting Image De-Raining via Central-Surrounding Synergistic Convolution (AAAI 2025 · similarity 0.91)
- Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining (CVPR 2024 · similarity 0.88)
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