1 Introduction

"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.

2 How to use

2.1 Prepare dataset

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
                    .
                    .

2.2 Train/Test

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}

3 Results

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:

4 Model Download

model	dataset
PReNet	RainH.zip

References

• 1. Progressive Image Deraining Networks: A Better and Simpler Baseline

@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},
 }