The code for Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution, the other models shown in the experiments could be refered to Remote-Sensing-Super-resolution-Model-Collection.
Models
Dataset
The experimental datasets, OLI2MSI and Alsat, could be obtained from:
Train
python src/main.py
Test
python test.py
Weight
Our pre-train ASDDPM and RRDB model on OLI2MSI and ALSAT could be downloaded from link:https://pan.baidu.com/s/1siWepPn2pVFC3SGyk1wuJg code:bean
A guidance file is also shared in this link, please put the pre-train model to the right place according to the guidance. (Attention: the code could run only after the RRDB pre-train model is put in the right place.)
@article{sui2024adaptive,
title={Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution},
author={Sui, Jialu and Ma, Xianping and Zhang, Xiaokang and Pun, Man-On},
journal={arXiv preprint arXiv:2403.11078},
year={2024}
}
@article{sui2023gcrdn,
title={Gcrdn: Global context-driven residual dense network for remote sensing image super-resolution},
author={Sui, Jialu and Ma, Xianping and Zhang, Xiaokang and Pun, Man-On},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2023},
publisher={IEEE}
}
@article{sui2024denoising,
title={Denoising Diffusion Probabilistic Model with Adversarial Learning for Remote Sensing Super-Resolution},
author={Sui, Jialu and Wu, Qianqian and Pun, Man-On},
journal={Remote Sensing},
volume={16},
number={7},
pages={1219},
year={2024},
publisher={MDPI}
}
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