EnlightenGAN: Deep Light Enhancement without Paired Supervision

Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

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!

Faster Inference

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}
}