| --- |
| license: mit |
| pipeline_tag: image-to-image |
| --- |
| |
| # DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models |
|
|
| 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: |
| 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. |
| 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). |
|
|
| ## 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: |
|
|
| ```bash |
| # Create a virtual environment (optional but recommended) Python version used 3.11.4 |
| python -m venv .venv |
| source .venv/bin/activate # Windows: venv\Scripts\activate |
| |
| # Install all Python dependencies |
| pip install -r requirements.txt |
| ``` |
|
|
| Then, download the pretrained checkpoints from the Hugging Face Hub using `git lfs`: |
|
|
| ```bash |
| git lfs install |
| git clone https://huggingface.co/NorskRegnesentralSTI/DiffFuSR && mv DiffFuSR/logs logs |
| ``` |
|
|
| 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: |
|
|
| ```bash |
| python 2_test_rgb_for_opensr_metric.py --checkpoint logs/blindsrsnf_aniso_worldstrat_degraded_harmfac_10000_large/version_7/checkpoints/last.ckpt |
| ``` |
| 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). |
|
|
| ## Citation |
|
|
| If you use this pipeline in your research, please cite our paper: |
|
|
| ```bibtex |
| @article{, |
| title={DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models}, |
| author={{}}, |
| year={2025}, |
| eprint={2506.11764}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2506.11764}, |
| } |
| ``` |
