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| Verfasst von: | Zharov, Yaroslav [VerfasserIn]  |
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| | Ametova, Evelina [VerfasserIn] |
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| | Spiecker, Rebecca [VerfasserIn] |
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| | Baumbach, Tilo [VerfasserIn] |
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| | Burca, Genoveva [VerfasserIn] |
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| | Heuveline, Vincent [VerfasserIn] |
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| Titel: | Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning |
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| Verf.angabe: | Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach, Genoveva Burca, and Vincent Heuveline |
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| E-Jahr: | 2023 |
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| Jahr: | Jul 2023 |
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| Umfang: | 19 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Online veröffentlicht am 24. Juli 2023 ; Gesehen am 07.12.2023 |
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| Titel Quelle: | Enthalten in: Optics express |
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| Ort Quelle: | Washington, DC : Optica, 1997 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 31(2023), 16, Seite 26226-26244 |
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| ISSN Quelle: | 1094-4087 |
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| Abstract: | Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography. |
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| DOI: | doi:10.1364/OE.492221 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1364/OE.492221 |
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| | Volltext: https://opg.optica.org/oe/abstract.cfm?uri=oe-31-16-26226 |
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| | DOI: https://doi.org/10.1364/OE.492221 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Image processing |
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| | Image quality |
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| | Image reconstruction |
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| | Medical imaging |
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| | Multichannel imaging |
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| | Phase shift |
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| K10plus-PPN: | 1861508034 |
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| Verknüpfungen: | → Zeitschrift |
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Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning / Zharov, Yaroslav [VerfasserIn]; Jul 2023 (Online-Ressource)
