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HAAM-GAN

This Repo includes the testing codes of our HAAM-GAN. (PyTorch Version).

If you use our code, please cite our paper and hit the star at the top-right corner. Thanks!

Requirement

Python 3.7, Pytorch 1.11.0.

Testing

1. Download the code
2. Put your testing images in the "data/input" folder
3. Python test.py
4. Find the result in "data/ouput" folder
5. You can find all the pre-trained model in https://drive.google.com/drive/folders/1h4OI-DIY0vgrjM2QrQXAyV3041xN8aHr?usp=sharing
Note that the PSNR_SSIM_UIQM.py provide the metrics code adopted our paper.

The validation data are in the "data/input" folder (underwater images), "data/gt" folder (grount truth images).

Bibtex

@article{HAAMGAN,
  title={Hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement},
  author={Zhang, Dehuan and Wu, Chenyu and Zhou, Jingchun and Zhang, Weishi and Li, Chaolei and Lin, Zifan},
  journal={Engineering Applications of Artificial Intelligence},
  volume={125},
  pages={106743},
  year={2023},
  publisher={Elsevier}
}

License

The code is made available for academic research purpose only. This project is open sourced under MIT license.

Contact

If you have any questions, please contact Jingchun Zhou at zhoujingchun03@qq.com.