QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911769697452032 |
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| author | Mauro, Francesco Sebastianelli, Alessandro Del Rosso, Maria Pia Gamba, Paolo Ullo, Silvia Liberata |
| author_facet | Mauro, Francesco Sebastianelli, Alessandro Del Rosso, Maria Pia Gamba, Paolo Ullo, Silvia Liberata |
| contents | The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at any time of day or night. However, SAR imagery quality is often compromised by speckle, a granular disturbance that poses challenges in producing accurate results without suitable data processing. In this context, the present paper explores the cutting-edge application of Quantum Machine Learning (QML) in speckle filtering, harnessing quantum algorithms to address computational complexities. We introduce here QSpeckleFilter, a novel QML model for SAR speckle filtering. The proposed method compared to a previous work from the same authors showcases its superior performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on a testing dataset, and it opens new avenues for Earth Observation (EO) applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering Mauro, Francesco Sebastianelli, Alessandro Del Rosso, Maria Pia Gamba, Paolo Ullo, Silvia Liberata Signal Processing The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at any time of day or night. However, SAR imagery quality is often compromised by speckle, a granular disturbance that poses challenges in producing accurate results without suitable data processing. In this context, the present paper explores the cutting-edge application of Quantum Machine Learning (QML) in speckle filtering, harnessing quantum algorithms to address computational complexities. We introduce here QSpeckleFilter, a novel QML model for SAR speckle filtering. The proposed method compared to a previous work from the same authors showcases its superior performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on a testing dataset, and it opens new avenues for Earth Observation (EO) applications. |
| title | QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2402.01235 |