# PairLIE

**Repository Path**: tlwzzy/PairLIE

## Basic Information

- **Project Name**: PairLIE
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-17
- **Last Updated**: 2025-03-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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. 

<div align=center><img src="img/1.png" height = "60%" width = "60%"/></div>

## 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}
}
```