GuidedHybSensUIR

Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis

Xiaojiao Guo ^{👨‍💻‍} , Xuhang Chen ^{👨‍💻‍} , Shuqiang Wang ^📮 , Chi-Man Pun ^📮 ( 👨‍💻‍ Equal contributions, 📮 Corresponding author)

University of Macau, SIAT CAS, Huizhou Univeristy, Baoshan Univeristy

In IEEE Transactions on Circuits and Systems for Video Technology

🔮 Benchmark Dataset

Kaggle

If you need visualization results, you may contact Dr.Guo via yc27441@um.edu.mo

⚙️ Usage

Training

You may download the dataset first, and then specify TRAIN_DIR, VAL_DIR and SAVE_DIR in the section TRAINING in config.yml.

For single GPU training:

python train.py

For multiple GPUs training:

accelerate config
accelerate launch train.py

If you have difficulties with the usage of accelerate, please refer to Accelerate.

Inference

Please first specify TRAIN_DIR, VAL_DIR and SAVE_DIR in section TESTING in config.yml.

python infer.py

Citation

@ARTICLE{10824878,
  author={Guo, Xiaojiao and Chen, Xuhang and Wang, Shuqiang and Pun, Chi-Man},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Image restoration;Image color analysis;Benchmark testing;Deep learning;Green products;Distortion;Transformers;Adaptation models;Training;Lighting;Underwater image restoration;image enhancement;prior guided attention;efficient Transformer;multi-scales hybridization},
  doi={10.1109/TCSVT.2025.3525593}
}