EDiffSR (IEEE TGRS 2024)

📖Paper | 🖼️PDF

PyTorch codes for "EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution", IEEE Transactions on Geoscience and Remote Sensing, 2024.

• Authors: Yi Xiao, Qiangqiang Yuan*, Kui Jiang, Jiang He, Xianyu Jin, and Liangpei Zhang<br>
• Wuhan University and Harbin Institute of Technology

🧩Usage

Dataset Preparation

Step I. Please download the following remote sensing benchmarks: | Data Type | AID | DOTA-v1.0 | DIOR | NWPU-RESISC45 | :----: | :-----: | :----: | :----: | :----: | |Training | Download | None | None | None | |Testing | Download | Download | Download | Download

🚩Please refer to Dataset Processing to build the LR-HR training pairs.

Step II. Modify the path in options/train/setting.yml and options/test/aid.yml

Train

python train.py -opt=options/train/setting.yml

Test

python test.py -opt=options/test/nwpu.yml

Acknowledgments

Our EDiffSR mainly borrows from the SDE process of IRSDE and NAFNet. Thanks for these excellent open-source works!

Contact

If you have any questions or suggestions, feel free to contact me. 😊
Email: xiao_yi@whu.edu.cn; xy574475@gmail.com

Citation

If our work contributes to your research, we would greatly appreciate your citation. Thank you for your support! 😊

@ARTICLE{xiao2024ediffsr,
  author={Xiao, Yi and Yuan, Qiangqiang and Jiang, Kui and He, Jiang and Jin, Xianyu and Zhang, Liangpei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution}, 
  year={2024},
  volume={62},
  number={},
  pages={1-14},
  doi={10.1109/TGRS.2023.3341437}
}