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Unsupervised Underwater Image Restoration: From a Homology Perspective (AAAI 2022)(Paper)
The Pytorch Implementation of ''Unsupervised Underwater Image Restoration: From a Homology Perspective''.
<div align=center><img src="img/1.png" height = "80%" width = "80%"/></div>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},
}