https://github.com/vita-group/enlightengan

[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
https://github.com/vita-group/enlightengan

gan generative-adversarial-networks low-light low-light-enhance pytorch unsupervised-learning

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[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

• Host: GitHub
• URL: https://github.com/vita-group/enlightengan
• Owner: VITA-Group
• License: other
• Created: 2019-06-16T13:14:30.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-04-25T07:46:42.000Z (7 months ago)
• Last Synced: 2024-08-11T17:09:20.472Z (3 months ago)
• Topics: gan, generative-adversarial-networks, low-light, low-light-enhance, pytorch, unsupervised-learning
• Language: Python
• Homepage:
• Size: 17.4 MB
• Stars: 900
• Watchers: 20
• Forks: 198
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: License

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README

        
# EnlightenGAN: Deep Light Enhancement without Paired Supervision

[Yifan Jiang](https://yifanjiang19.github.io/), Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

[[Paper]](https://arxiv.org/abs/1906.06972) [[Supplementary Materials]](https://yifanjiang.net/files/EnlightenGAN_Supplementary.pdf)

### Representitive Results

![representive_results](/assets/show_3.png)

### Overal Architecture

![architecture](/assets/arch.png)

## 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]](https://drive.google.com/file/d/1IfCeihmPqGWJ0KHmH-mTMi_pn3z3Zo-P/view?usp=sharing), 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](https://drive.google.com/file/d/1AkV-n2MdyfuZTFvcon8Z4leyVb0i7x63/view?usp=sharing) 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]](https://drive.google.com/drive/folders/1KivxOm79VidSJnJrMV9osr751UD68pCu?usp=sharing) (unpaired images collected from multiple datasets)

Testing data [[Google Drive]](https://drive.google.com/drive/folders/1PrvL8jShZ7zj2IC3fVdDxBY1oJR72iDf?usp=sharing) (including LIME, MEF, NPE, VV, DICP)

And [[BaiduYun]](https://github.com/TAMU-VITA/EnlightenGAN/issues/28) 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}

}

```