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!
Python 3.7, Pytorch 1.11.0.
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).
@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}
}
The code is made available for academic research purpose only. This project is open sourced under MIT license.
If you have any questions, please contact Jingchun Zhou at zhoujingchun03@qq.com.