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.

To install dependencies:

pip install -r requirements.txt

To download datasets:

• Sen2_MTC_Old: multipleImage.tar.gz

• Sen2_MTC_New: CTGAN.zip

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

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

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

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

To evaluate my models on two datasets, run:

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

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},
}