PairLIE

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- 1.png
- net.py
- PairLIE.pth
data.py
dataset.py
eval.py
main.py
measure.py
README.md
utils.py

// repository documentation

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# Learning a Simple Low-light Image Enhancer from Paired Low-light Instances (CVPR 2023)([Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.pdf))
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](https://drive.google.com/file/d/1gM3QeNDOCzx0m1gpOoQD1TnGv1BELy08/view?usp=sharing).

### 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}
}
```

PairLIE

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