Our work proposes an “Adaptive learning attention network for underwater image enhancement” (LANet) to solve the problem of color casts and low illumination in underwater images.

1. Download the code
2. run Python train.py --input_images-path ./data/trainA/ --label_images_path ./data/trainB/ 
3. Find checkpoint in the "./checkpoints/" folder
The training data includes input data and label data. input data are in the "./data/trainA" folder, label data are in the "./data/trainB" folder

1. pre-trained models in the "./checkpoints/" folder
2. Put your testing images in the "./data/test/" folder 
3. run Python test.py --test_pth ./data/test/ --snapshot_pth ./checkpoints/model_epoch_40.pk
4. Find the result in "./results" folder

@article{liu2022adaptive,
  title={Adaptive learning attention network for underwater image enhancement},
  author={Liu, Shiben and Fan, Huijie and Lin, Sen and Wang, Qiang and Ding, Naida and Tang, Yandong},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={2},
  pages={5326--5333},
  year={2022},
  publisher={IEEE}
}