Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior

Implementation

• Python3
• PyTorch>=1.0
• NVIDIA GPU+CUDA

Training process

The original LOL dataset can be downloaded from here. The EnlightenGAN dataset can be downloaded from here Before starting training process, you should modify the data_root in ./config, and then run the following command

python LUM_train.py
python NDM_train.py

Testing process

Please put test images into 'test_images' folder and download the pre-trained checkpoints from google drive(put it into ./checkpoints), then just run

python NDM_test.py

You can also just evaluate the stage one (LUM), just run

python LUM_test.py

Paper Summary

HEP consists of two stages, Light Up Module (LUM) and Noise Disentanglement Module (LUM)

Representative Visual Results

More visual results can be found in asssets.

Citation

if you find this repo is helpful, please cite

@article{zhang2021unsupervised,
  title={Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior},
  author={Zhang, Feng and Shao, Yuanjie and Sun, Yishi and Zhu, Kai and Gao, Changxin and Sang, Nong},
  journal={arXiv preprint arXiv:2112.01766},
  year={2021}
}