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Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning

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 energ...

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Hauptverfasser: Zharov, Yaroslav (Verfasst von) , Ametova, Evelina (Verfasst von) , Spiecker, Rebecca (Verfasst von) , Baumbach, Tilo (Verfasst von) , Burca, Genoveva (Verfasst von) , Heuveline, Vincent (Verfasst von)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: Jul 2023
In: Optics express
Year: 2023, Jahrgang: 31, Heft: 16, Pages: 26226-26244
ISSN:1094-4087
DOI:10.1364/OE.492221
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1364/OE.492221
Verlag, lizenzpflichtig, Volltext: https://opg.optica.org/oe/abstract.cfm?uri=oe-31-16-26226
Volltext
Verfasserangaben:Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach, Genoveva Burca, and Vincent Heuveline
Beschreibung
Zusammenfassung: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.
Beschreibung:Online veröffentlicht am 24. Juli 2023
Gesehen am 07.12.2023
Beschreibung:Online Resource
ISSN:1094-4087
DOI:10.1364/OE.492221

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