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--- |
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license: mit |
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pipeline_tag: image-to-image |
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library_name: diffusers |
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--- |
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# DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models |
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This model is presented in the paper [DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models](https://huggingface.co/papers/2506.11764). |
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DiffFuSR is a modular pipeline designed for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.5 meters. The pipeline operates in two stages: |
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1. A diffusion-based super-resolution (SR) model trained on high-resolution RGB imagery from the NAIP and WorldStrat datasets, harmonized to simulate Sentinel-2 characteristics. |
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2. A learned fusion network that upscales the remaining multispectral bands by utilizing the super-resolved RGB image as a spatial prior. |
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This approach introduces a robust degradation model and contrastive degradation encoder to support blind SR, outperforming current SOTA baselines in reflectance fidelity, spectral consistency, spatial alignment, and hallucination suppression. |
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The official implementation, including code and training/evaluation scripts, can be found on GitHub: [https://github.com/NorskRegnesentral/DiffFuSR](https://github.com/NorskRegnesentral/DiffFuSR). |
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## Sample Usage |
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To use the DiffFuSR models, you can follow the instructions provided in the official GitHub repository. |
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First, set up your Python environment and install dependencies: |
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```bash |
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# Create a virtual environment (optional but recommended) Python version used 3.11.4 |
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python -m venv .venv |
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source .venv/bin/activate # Windows: venv\Scripts\activate |
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# Install all Python dependencies |
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pip install -r requirements.txt |
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``` |
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Then, download the pretrained checkpoints from the Hugging Face Hub using `git lfs`: |
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```bash |
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git lfs install |
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git clone https://huggingface.co/NorskRegnesentralSTI/DiffFuSR && mv DiffFuSR/logs logs |
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``` |
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After downloading, you can run one of the provided test scripts. For example, to test RGB SR for OpenSR metric using the WorldStrat SR model: |
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```bash |
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python 2_test_rgb_for_opensr_metric.py --checkpoint logs/blindsrsnf_aniso_worldstrat_degraded_harmfac_10000_large/version_7/checkpoints/last.ckpt |
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``` |
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You can change the `--checkpoint` flag to test other available models (NAIP-no-harm SR or NAIP-harm SR). |
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More detailed usage and training instructions are available in the [GitHub repository](https://github.com/NorskRegnesentral/DiffFuSR). |
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## Citation |
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If you use this pipeline in your research, please cite our paper: |
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```bibtex |
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@article{, |
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title={DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models}, |
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author={{}}, |
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year={2025}, |
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eprint={2506.11764}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2506.11764}, |
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} |
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``` |
