[CVIU 2024] Revisiting Coarse-to-fine Strategy for Low-Light Image Enhancement with Deep Decomposition Guided Training [Paper].

Hai Jiang¹, Yang Ren¹, Songchen Han^1,†

¹School of Aeronautics and Astronautics, Sichuan University

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.]

Pipeline

Pre-trained Models

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

How to train?

python main.py --mode train

How to test?

python main.py --mode test

Citation

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

@article{jiang2023low,
  title={Revisiting Coarse-to-fine Strategy for Low-Light Image Enhancement with Deep Decomposition Guided Training},
  author={Jiang, Hai and Ren, Yang and Han, Songchen},
  journal={Computer Vision and Image Understanding},
  volume = {241},
  pages = {103952},
  year = {2024}
}

Acknowledgement

Part of the code is adapted from the previous work: MIMO-UNet. We thank all the authors for their contributions.