Controllable Reference-Guided Diffusion with Local–Global Fusion for Real-World Remote Sensing Image Super-Resolution
Ce Wang1, Wanjie Sun1
1School of Remote Sensing and Information Engineering, Wuhan University
# Clone this repository
git clone https://github.com/wwangcece/CRefDiff.git
# Create a conda environment with Python >= 3.9
conda create -n CRefDiff python=3.9
conda activate CRefDiff
# Install required packages
pip install -r requirements.txtDownload the pretrained models from the link below and place them in the checkpoints/ directory:
- Refer to the Real-RefRSSRD for downloading.
- Use the script
dataset/prepare_lr.pyto upscale the LR images to match the size of HR.
- Modify the validation dataset configuration in
configs/dataset/reference_sr_test.yamland update the pretrained model path ininference_refsr_batch.py. - Run the inference script:
python inference_refsr_batch.sh --ckpt path/to/pretrained/model --output path/tp/out/dir --global_ref_scale 1 --device cuda:0 If you find this work helpful, please consider citing:
@misc{wang2025controllablereferencebasedrealworldremote,
title={Controllable Reference-Based Real-World Remote Sensing Image Super-Resolution with Generative Diffusion Priors},
author={Ce Wang and Wanjie Sun},
year={2025},
eprint={2506.23801},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.23801},
}This project is based on DiffBIR. We thank the authors for their excellent work.
If you have any questions, feel free to reach out to: Ce Wang — cewang@whu.edu.cn





