Learning a Simple Low-light Image Enhancer from Paired Low-light Instances (CVPR 2023)(Paper)

The Pytorch Implementation of PairLIE.

Introduction

In this project, we use Ubuntu 16.04.5, Python 3.7, Pytorch 1.12.0 and one NVIDIA RTX 2080Ti GPU.

Datasets and results

Training dataset, testing dataset, and our predictions are available at Google Drive.

Testing

The pretrained model is in the ./weights.

Check the model and image pathes in eval.py, and then run:

python eval.py

Training

To train the model, you need to prepare our training dataset.

Check the dataset path in main.py, and then run:

python main.py

Citation

If you find PairLIE is useful in your research, please cite our paper:

@inproceedings{fu2023learning,
  title={Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances},
  author={Fu, Zhenqi and Yang, Yan and Tu, Xiaotong and Huang, Yue and Ding, Xinghao and Ma, Kai-Kuang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={22252--22261},
  year={2023}
}