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"Progressive Image Deraining Networks: A Better and Simpler Baseline" provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.
The dataset(RainH.zip) used by PReNet can be downloaded from here,uncompress it and get two folders(RainTrainH、Rain100H).
The structure of dataset is as following:
├── RainH
├── RainTrainH
| ├── rain
| | ├── 1.png
| | └── 2.png
| | .
| | .
| └── norain
| ├── 1.png
| └── 2.png
| .
| .
└── Rain100H
├── rain
| ├── 001.png
| └── 002.png
| .
| .
└── norain
├── 001.png
└── 002.png
.
.
train model:
python -u tools/main.py --config-file configs/prenet.yaml
test model:
python tools/main.py --config-file configs/prenet.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
Evaluated on RGB channels, scale pixels in each border are cropped before evaluation.
The metrics are PSNR / SSIM.
Method | Rain100H |
---|---|
PReNet | 29.5037 / 0.899 |
Input:
Output:
model | dataset |
---|---|
PReNet | RainH.zip |
@inproceedings{ren2019progressive,
title={Progressive Image Deraining Networks: A Better and Simpler Baseline},
author={Ren, Dongwei and Zuo, Wangmeng and Hu, Qinghua and Zhu, Pengfei and Meng, Deyu},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019},
}
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