https://github.com/JianghaiSCU/Diffusion-Low-Light
Official pytorch implementation for "Low-light Image Enhancement with Wavelet-based Diffusion Models"
https://github.com/JianghaiSCU/Diffusion-Low-Light
Last synced: 8 months ago
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Official pytorch implementation for "Low-light Image Enhancement with Wavelet-based Diffusion Models"
- Host: GitHub
- URL: https://github.com/JianghaiSCU/Diffusion-Low-Light
- Owner: JianghaiSCU
- License: mit
- Created: 2023-08-07T08:05:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-08T14:31:07.000Z (over 1 year ago)
- Last Synced: 2024-10-30T22:40:13.719Z (about 1 year ago)
- Language: Python
- Size: 20.6 MB
- Stars: 181
- Watchers: 4
- Forks: 26
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-diffusion-categorized - [Code
README
# [Siggraph Asia 2023]Low-light Image Enhancement with Wavelet-based Diffusion Models [[Paper]](https://arxiv.org/pdf/2306.00306.pdf).
Hai Jiang1,2, Ao Luo2, Haoqiang Fan2, Songchen Han1, Shuaicheng Liu3,2
1.Sichuan University, 2.Megvii Technology,
3.University of Electronic Science and Technology of China
## 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)]](https://pan.baidu.com/s/1spt0kYU3OqsQSND-be4UaA) [[Google Drive]](https://drive.google.com/file/d/18bs_mAREhLipaM2qvhxs7u7ff2VSHet2/view?usp=sharing)
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)]](https://pan.baidu.com/s/1U9ePTfeLlnEbr5dtI1tm5g) [[Google Drive]](https://drive.google.com/file/d/1dzuLCk9_gE2bFF222n3-7GVUlSVHpMYC/view?usp=sharing)
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)]](https://pan.baidu.com/s/1XHWQAS0ZNrnCyZ-bq7MKvA)
### Unpaired datasets
Please refer to [[Project Page of RetinexNet.]](https://daooshee.github.io/BMVC2018website/)
## Pre-trained Models
You can downlaod our pre-trained model from [[Google Drive]](https://drive.google.com/file/d/1f4zDvPsWKrID33OJdeHwc5VOBILkm0KW/view?usp=sharing) and [[Baidu Yun (extracted code:wsw7)]](https://pan.baidu.com/s/1rq8VzdnHeky0iT56coOGog)
## 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](https://github.com/IGITUGraz/WeatherDiffusion), [SDWNet](https://github.com/FlyEgle/SDWNet), and [MIMO-UNet](https://github.com/chosj95/MIMO-UNet). We thank all the authors for their contributions.