https://github.com/JianghaiSCU/LightenDiffusion
Official pytorch implementation for "LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models"
https://github.com/JianghaiSCU/LightenDiffusion
Last synced: about 1 month ago
JSON representation
Official pytorch implementation for "LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models"
- Host: GitHub
- URL: https://github.com/JianghaiSCU/LightenDiffusion
- Owner: JianghaiSCU
- Created: 2024-07-09T06:14:01.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-07-24T08:09:15.000Z (10 months ago)
- Last Synced: 2024-10-30T22:40:13.355Z (6 months ago)
- Language: Python
- Size: 4.58 MB
- Stars: 115
- Watchers: 3
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-diffusion-categorized - [Code
README
# [ECCV 2024] LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models [[Paper]](https://arxiv.org/pdf/2407.08939)
Hai Jiang1,5, Ao Luo2,5, Xiaohong Liu4, Songchen Han1, Shuaicheng Liu3,5
1.Sichuan University, 2.Southwest Jiaotong University,
3.University of Electronic Science and Technology of China,
4.Shanghai Jiaotong University, 5.Megvii Technology
## Pipeline
## Dependencies
```
pip install -r requirements.txt
````
## Download the raw training and evaluation datasets
### Paired datasets
LOL 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)
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 download our pre-trained model from [[Google Drive]](https://drive.google.com/drive/folders/1m3t15rWw76IDDWJ0exLOe5P0uEnjk3zl?usp=drive_link) and [[Baidu Yun (extracted code:cjzk)]](https://pan.baidu.com/s/1fPLVgnZbdY1n75Flq54bMQ)
## 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:
```
@InProceedings{Jiang_2024_ECCV,
author = {Jiang, Hai and Luo, Ao and Liu, Xiaohong and Han, Songchen and Liu, Shuaicheng},
title = {LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models},
booktitle = {European Conference on Computer Vision},
year = {2024},
pages = {}
}
```
## Acknowledgement
Part of the code is adapted from previous works: [WeatherDiff](https://github.com/IGITUGraz/WeatherDiffusion) and [MIMO-UNet](https://github.com/chosj95/MIMO-UNet). We thank all the authors for their contributions.