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README.md
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license: mit
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---
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="
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</p>
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<p align="center">
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To address these challenges, 'opensr-test' provides a fair approach for SR benchmark. We provide three datasets carefully crafted to minimize spatial and spectral misalignment. Besides, 'opensr-test' precisely assesses SR algorithm performance across three independent metrics groups that measure consistency, synthesis, and correctness.
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<p align="center">
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<img src="
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</p>
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```
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="
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</p>
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### **SPOT (X4 scale factor)**
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```
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="
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</p>
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```
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="
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</p>
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## **Deeper understanding**
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Explore the [API](https://esaopensr.github.io/opensr-test/docs/API/config_pydantic.html) section for more details about personalizing your benchmark experiments.
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<p align="center">
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<a href="/docs/api.md"><img src="
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</p>
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## **Citation**
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## **Acknowledgements**
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This work was made with the support of the European Space Agency (ESA) under the project “Explainable AI: application to trustworthy super-resolution (OpenSR)”. Cesar Aybar acknowledges support by the National Council of Science, Technology, and Technological Innovation (CONCYTEC, Peru) through the “PROYECTOS DE INVESTIGACIÓN BÁSICA – 2023-01” program with contract number PE501083135-2023-PROCIENCIA. Luis Gómez-Chova acknowledges support from the Spanish Ministry of Science and Innovation (project PID2019-109026RB-I00 funded by MCIN/AEI/10.13039/501100011033).
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---
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license: mit
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task_categories:
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- image-to-image
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language:
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- en
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tags:
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- earth
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- remote sensing
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- super-resolution
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- Sentinel-2
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- sentinel-2
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pretty_name: opensr_test
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size_categories:
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- 10K<n<100K
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/logo.png" alt="header" width="50%"></a>
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</p>
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<p align="center">
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To address these challenges, 'opensr-test' provides a fair approach for SR benchmark. We provide three datasets carefully crafted to minimize spatial and spectral misalignment. Besides, 'opensr-test' precisely assesses SR algorithm performance across three independent metrics groups that measure consistency, synthesis, and correctness.
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<p align="center">
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<img src="images/diagram.png" alt="header">
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</p>
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```
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/NAIP.gif" alt="header" width="80%"></a>
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</p>
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### **SPOT (X4 scale factor)**
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```
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/SPOT.gif" alt="header" width="80%"></a>
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</p>
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```
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<p align="center">
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<a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/VENUS.gif" alt="header" width="80%"></a>
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</p>
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## **Deeper understanding**
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Explore the [API](https://esaopensr.github.io/opensr-test/docs/API/config_pydantic.html) section for more details about personalizing your benchmark experiments.
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<p align="center">
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<a href="/docs/api.md"><img src="images/image02.png" alt="opensr-test" width="30%"></a>
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</p>
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## **Citation**
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## **Acknowledgements**
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This work was made with the support of the European Space Agency (ESA) under the project “Explainable AI: application to trustworthy super-resolution (OpenSR)”. Cesar Aybar acknowledges support by the National Council of Science, Technology, and Technological Innovation (CONCYTEC, Peru) through the “PROYECTOS DE INVESTIGACIÓN BÁSICA – 2023-01” program with contract number PE501083135-2023-PROCIENCIA. Luis Gómez-Chova acknowledges support from the Spanish Ministry of Science and Innovation (project PID2019-109026RB-I00 funded by MCIN/AEI/10.13039/501100011033).
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