This is an implement of the TOPAL, “Target Oriented Perceptual Adversarial Fusion Network for Underwater Image Enhancement”, Zhiying Jiang, Zhuoxiao Li, Shuzhou Yang, Xin Fan, Risheng Liu*, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2022.

Clone this repo:

conda create -n TOPAL python=3.7
conda activate TOPAL
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip3 install thop matplotlib scikit-image opencv-python yacs joblib natsort h5py tqdm

Download the pre-trained model and put it in networks/model

• Baidu Yun
  code: ​ vtab ​
• Google Drive

Put the images you want to process in the Underwater folder.
To test the pre-trained models for Underwater Enhancement on your own images, run ​ python main.py ​
Results will be shown in Result folder.

If you use TOPAL, please consider citing:

@ARTICLE{TOPAL,
 author={Jiang, Zhiying and Li, Zhuoxiao and Yang, Shuzhou and Fan, Xin and Liu, Risheng},
 journal={IEEE Transactions on Circuits and Systems for Video Technology},
 title={Target Oriented Perceptual Adversarial Fusion Network for Underwater Image Enhancement},
 year={2022},
 pages={1-1},
 doi={10.1109/TCSVT.2022.3174817}}

Should you have any question, please contact Zhiying Jiang.