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UnSupervised underwater image restoration

USUIR 최신버젼 다운로드

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// repository documentation

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# Unsupervised Underwater Image Restoration: From a Homology Perspective (AAAI 2022)([Paper](https://ojs.aaai.org/index.php/AAAI/article/view/19944))
The Pytorch Implementation of ''Unsupervised Underwater Image Restoration: From a Homology Perspective''.

## Introduction
In this project, we use Ubuntu 16.04.5, Python 3.7, Pytorch 1.7.1 and one NVIDIA RTX 2080Ti GPU.

## Running

### Testing

The pretrained models are in ./final_weight.

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

```
python eval.py
```

### Training

To train the model, you need to first prepare our [dataset](https://drive.google.com/file/d/1DBCXCa5GWJPB7S6xO7f0N562FqXhsV6c/view?usp=sharing).

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

## Citation

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

```
@inproceedings{Fu_Lin_Yang_Chai_Sun_Huang_Ding_2022, 
	title={Unsupervised Underwater Image Restoration: From a Homology Perspective}, 
	author={Fu, Zhenqi and Lin, Huangxing and Yang, Yan and Chai, Shu and Sun, Liyan and Huang, Yue and Ding, Xinghao}, 
	booktitle={AAAI Conference on Artificial Intelligence (AAAI)},
	year={2022}, 
	volume={36}, 
	pages={643-651},	
}
```

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