EfficientDerain

we propose EfficientDerain for high-efficiency single-image deraining

Requirements

• python 3.6
• pytorch 1.6.0
• opencv-python 4.4.0.44
• scikit-image 0.17.2

Datasets

• Rain100L-old_version https://github.com/nnUyi/DerainZoo/blob/master/DerainDatasets.md
• Rain100H-old_version https://github.com/nnUyi/DerainZoo/blob/master/DerainDatasets.md
• Rain1400 https://xueyangfu.github.io/projects/cvpr2017.html
• SPA https://stevewongv.github.io/derain-project.html

Pretrained models

Here is the urls of pretrained models (includes v3_rain100H, v3_rain1400, v3_SPA, v4_rain100H, v4_rain1400, v4_SPA) :

direct download: http://www.xujuefei.com/models_effderain.zip

google drive: https://drive.google.com/file/d/1OBAIG4su6vIPEimTX7PNuQTxZDjtCUD8/view?usp=sharing

baiduyun: https://pan.baidu.com/s/1kFWP-b3tD8Ms7VCBj9f1kw (pwd: vr3g)

Train

• The code shown corresponds to version v3, for v4 change the value of argument "rainaug" in file "./train.sh" to the "true" (You need to unzip the "Streaks_Garg06.zip" in the "./rainmix")
• Change the value of argument "baseroot" in file "./train.sh" to the path of training data
• Edit the function "get_files" in file "./utils" according to the format of the training data
• Execute

sh train.sh

Test

• The code shown corresponds to version v3
• Change the value of argument "load_name" in file "./test.sh" to the path of pretained model
• Change the value of argument "baseroot" in file "./test.sh" to the path of testing data
• Edit the function "get_files" in file "./utils" according to the format of the testing data
• Execute

sh test.sh

Results

The specific results can be found in “./results/data/DERAIN.xlsx”

GT vs RCDNet	GT vs EfDeRain	Input vs GT
GT vs RCDNet	GT vs EfDeRain	Input vs GT

GT vs v1	GT vs v2	GT vs v3	GT vs v4
GT vs v1	GT vs v2	GT vs v3	GT vs v4

Bibtex

@inproceedings{guo2020efficientderain,
      title={EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining}, 
      author={Qing Guo and Jingyang Sun and Felix Juefei-Xu and Lei Ma and Xiaofei Xie and Wei Feng and Yang Liu},
      year={2021},
      booktitle={AAAI}
}