# Clone the repository
git clone https://github.com/yourusername/SeaDiff.git
cd SeaDiff

# Create conda environment
conda create -n seadiff python=3.8
conda activate seadiff

📂 Dataset Preparation

To train SeaDiff, please follow these steps:

Download UIE datasets:
- UIEB
- EUVP
- SUIM-E
Generate depth maps using Depth Anything V2:

Create histogram representations:

python utils/create_hist_sample.py --input_dir datasets/UIEB/train/input --output_dir datasets/UIEB/train/histo

Dataset Structure

After preprocessing, organize your data as follows:

datasets/
└── UIEB/
    ├── train/
    │   ├── input/
    │   ├── label/
    │   ├── depth/
    │   └── histo/
    └── val/
        ├── input/
        ├── label/
        ├── depth/
        └── histo/

🚀 Quick Start

Training or Testing

Modify the configuration in conf.yml:

MODE: 1                    # 1 for training, 0 for inference
PRE_ORI: 'True'            # True for $x_0$, False for $\epsilon$
# ... other parameters

Start:
```
python main.py --conf conf.yml
```

📜 Citation

If you find our work useful, please cite:

@ARTICLE{11062889,
  author={Bi, Hengyue and Chen, Long and Cao, Jingchao and Wang, Jingyang and Sun, Jinghao and Rao, Yuan and Dong, Junyu},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={SeaDiff: Underwater Image Enhancement with Degradation-Aware Diffusion Model}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Image color analysis;Diffusion models;Degradation;Training;Imaging;Adaptation models;Image enhancement;Histograms;Feature extraction;Data mining;Underwater image enhancement;conditional diffusion models;prior knowledge},
  doi={10.1109/TCSVT.2025.3585429}}

🤝 Acknowledgements

Our code is based on the following excellent works:

We thank the authors for their outstanding contributions! 🙏

📧 Contact

If you have any questions, please feel free to:

📧 Email: bihengyue@stu.ouc.edu.cn
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