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
- Wuhan University and Harbin Institute of Technology
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 |
Step II. Modify the path in options/train/setting.yml and options/test/aid.yml
python train.py -opt=options/train/setting.yml
python test.py -opt=options/test/nwpu.yml
Our EDiffSR mainly borrows from the SDE process of IRSDE and NAFNet (https://github.com/megvii-research/NAFNet).
Thanks for these excellent open-source works!
If you have any questions or suggestions, feel free to contact me. 😊
Email: [email protected]; [email protected]
If you find our work helpful in your research, kindly consider citing it. We appreciate 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}
}
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