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[Siggraph Asia 2023]Low-light Image Enhancement with Wavelet-based Diffusion Models [Paper].

<h4 align="center">Hai Jiang<sup>1,2</sup>, Ao Luo<sup>2</sup>, Haoqiang Fan<sup>2</sup>, Songchen Han<sup>1</sup>, Shuaicheng Liu<sup>3,2</sup></center> <h4 align="center">1.Sichuan University, 2.Megvii Technology, <h4 align="center">3.University of Electronic Science and Technology of China</center></center>

Pipeline

Dependencies

pip install -r requirements.txt

Download the raw training and evaluation datasets

Paired datasets

LOLv1 dataset: Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. "Deep Retinex Decomposition for Low-Light Enhancement", BMVC, 2018. [Baiduyun (extracted code: sdd0)] [Google Drive]

LOLv2 dataset: Wenhan Yang, Haofeng Huang, Wenjing Wang, Shiqi Wang, and Jiaying Liu. "Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement", TIP, 2021. [Baiduyun (extracted code: l9xm)] [Google Drive]

LSRW dataset: Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu Zou, Fang Lin, and Songchen Han. "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network", Journal of Visual Communication and Image Representation, 2023. [Baiduyun (extracted code: wmrr)]

Unpaired datasets

Please refer to [Project Page of RetinexNet.]

Pre-trained Models

You can downlaod our pre-trained model from [Google Drive] and [Baidu Yun (extracted code:wsw7)]

How to train?

You need to modify datasets/dataset.py slightly for your environment, and then

python train.py

How to test?

python evaluate.py

Visual comparison

Citation

If you use this code or ideas from the paper for your research, please cite our paper:

@article{jiang2023low,
  title={Low-light image enhancement with wavelet-based diffusion models},
  author={Jiang, Hai and Luo, Ao and Fan, Haoqiang and Han, Songchen and Liu, Shuaicheng},
  journal={ACM Transactions on Graphics (TOG)},
  volume={42},
  number={6},
  pages={1--14},
  year={2023}
}

Acknowledgement

Part of the code is adapted from previous works: WeatherDiff, SDWNet, and MIMO-UNet. We thank all the authors for their contributions.