VITA-Group / Enlightengan
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[IEEE TIP'2021] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
Stars: ✭ 434
Programming Languages
python
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EnlightenGAN
IEEE Transaction on Image Processing, 2020, EnlightenGAN: Deep Light Enhancement without Paired Supervision
Representitive Results
Overal Architecture
Environment Preparing
python3.5
You should prepare at least 3 1080ti gpus or change the batch size.
pip install -r requirement.txt
mkdir model
Download VGG pretrained model from [Google Drive 1], and then put it into the directory model.
Training process
Before starting training process, you should launch the visdom.server for visualizing.
nohup python -m visdom.server -port=8097
then run the following command
python scripts/script.py --train
Testing process
Download pretrained model and put it into ./checkpoints/enlightening
Create directories ../test_dataset/testA and ../test_dataset/testB. Put your test images on ../test_dataset/testA (And you should keep whatever one image in ../test_dataset/testB to make sure program can start.)
Run
python scripts/script.py --predict
Dataset preparing
Training data [Google Drive] (unpaired images collected from multiple datasets)
Testing data [Google Drive] (including LIME, MEF, NPE, VV, DICP)
And [BaiduYun] is available now thanks to @YHLelaine!
If you find this work useful for you, please cite
@article{jiang2021enlightengan,
title={Enlightengan: Deep light enhancement without paired supervision},
author={Jiang, Yifan and Gong, Xinyu and Liu, Ding and Cheng, Yu and Fang, Chen and Shen, Xiaohui and Yang, Jianchao and Zhou, Pan and Wang, Zhangyang},
journal={IEEE Transactions on Image Processing},
volume={30},
pages={2340--2349},
year={2021},
publisher={IEEE}
}
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