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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


📚 Table of Contents


👁️ Visual Results

Results Real-RefRSSRD

For High Spatialtempoal Image Generation

Results of Global-Local Control


⚙️ Installation

# 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.txt

🧬 Pretrained Models

Download the pretrained models from the link below and place them in the checkpoints/ directory:

Download from HuggingFace


📊 Dataset

Geographic Coordinate Sampling Points

Data Samples

  1. Refer to the Real-RefRSSRD for downloading.
  2. Use the script dataset/prepare_lr.py to upscale the LR images to match the size of HR.

⚔️ Inference

  1. Modify the validation dataset configuration in configs/dataset/reference_sr_test.yaml and update the pretrained model path in inference_refsr_batch.py.
  2. 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 


📖 Citation

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}, 
}

🙏 Acknowledgements

This project is based on DiffBIR. We thank the authors for their excellent work.


📨 Contact

If you have any questions, feel free to reach out to: Ce Wangcewang@whu.edu.cn

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