A Comprehensive Benchmarking Framework for Sentinel-2 Sharpening: Methods, Dataset, and Evaluation Metrics: The advancement of super-resolution and sharpening algorithms for satellite images has significantly expanded the potential applications of remote sensing data. In the case of Sentinel-2, despite significant progress, the lack of standardized datasets and evaluation protocols has made it difficult to fairly compare existing methods and advance the state of the art. This work introduces a comprehensive benchmarking framework for Sentinel-2 sharpening, designed to address these challenges and foster future research. It analyzes several state-of-the-art sharpening algorithms, selecting representative methods ranging from traditional pansharpening to ad hoc model-based optimization and deep learning approaches. All selected methods have been re-implemented within a consistent Python-based framework and evaluated on a suitably designed, large-scale Sentinel-2 dataset. This dataset features diverse geographical regions, land cover types, and acquisition conditions, ensuring robust training and testing scenarios. The performance of the sharpening methods is assessed using both reference-based and no-reference quality indexes, highlighting strengths, limitations, and open challenges of current state-of-the-art algorithms. The proposed framework, dataset, and evaluation protocols are openly shared with the research community to promote collaboration and reproducibility

If you use this toolbox in your research, please use the following BibTeX entry.

@Article{rs17121983,
    AUTHOR = {Ciotola, Matteo and Guarino, Giuseppe and Mazza, Antonio and Poggi, Giovanni and Scarpa, Giuseppe},
    TITLE = {A Comprehensive Benchmarking Framework for Sentinel-2 Sharpening: Methods, Dataset, and Evaluation Metrics},
    JOURNAL = {Remote Sensing},
    VOLUME = {17},
    YEAR = {2025},
    NUMBER = {12},
    ARTICLE-NUMBER = {1983},
    URL = {https://www.mdpi.com/2072-4292/17/12/1983},
    ISSN = {2072-4292},
    DOI = {10.3390/rs17121983}
    }

Copyright (c) 2025 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA'). All rights reserved. This software should be used, reproduced and modified only for informational and nonprofit purposes.

By downloading and/or using any of these files, you implicitly agree to all the terms of the license, as specified in the document LICENSE (included in this package)

The dataset used is downable from the GRIP-UNINA website. However, we provide extensive instructions on how to download and elaborate the images correctly in the Dataset folder of this repository. For any problem or question, please contact me at If you have any problems or questions, please contact me by email (matteo.ciotola@unina.it).

All the functions and scripts were tested on Windows and Ubuntu O.S., with these constrains:

• Python 3.10.10
• PyTorch >= 2.0.0
• Cuda 11.8 (For GPU acceleration).

the operation is not guaranteed with other configurations.

• Install Anaconda and git
• Create a folder in which save the toolbox
• Download the toolbox and unzip it into the folder or, alternatively, from CLI:

git clone https://github.com/matciotola/Sentinel2-SR-Toolbox

• Create the virtual environment with the s2_sharp_toolbox_env.yml

conda env create -n s2_sharp_toolbox_env -f s2_sharp_toolbox_env.yml

• Activate the Conda Environment

conda activate s2_sharp_toolbox_env

• Edit the 'preamble.yaml' file with the correct paths and the desired algorithms to run

• Test it

python main_20.py
python main_60.py