Unsupervised Underwater Image Restoration: From a Homology Perspective (AAAI 2022)(Paper)

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.

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},	
}