[IEEE TIP] "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
Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
[Paper] [Supplementary Materials]
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
.
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
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
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!
https://github.com/arsenyinfo/EnlightenGAN-inference from @arsenyinfo
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}
}