README.md

DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images

This repository is the official PyTorch implementation of the TGRS 2024 paper DiffCR.

Thank you all for your attention to our work. I'm sorry for not responding quickly. Shortly, possibly within a month, we will specifically allocate time to organize all files related to the paper and open-source everything that can be open-sourced, including code, datasets, pre-trained models, and even visualization results, etc. Thank you again for your attention.

Requirements

To install dependencies:

pip install -r requirements.txt

To download datasets:

• Sen2_MTC_Old: multipleImage.tar.gz

• Sen2_MTC_New: CTGAN.zip

Training

To train the models in the paper, run these commands:

python run.py -p train -c config/ours_sigmoid.json

Testing

To test the pre-trained models in the paper, run these commands:

python run.py -p test -c config/ours_sigmoid.json

Evaluation

To evaluate my models on two datasets, run:

python evaluation/eval.py -s [ground-truth image path] -d [predicted-sample image path]

Citation

If you use our code or models in your research, please cite with:

@ARTICLE{diffcr,
  author={Zou, Xuechao and Li, Kai and Xing, Junliang and Zhang, Yu and Wang, Shiying and Jin, Lei and Tao, Pin},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal From Optical Satellite Images}, 
  year={2024},
  volume={62},
  number={},
  pages={1-14},
}

Acknowledgments