Journal
OPTICS EXPRESS
Volume 31, Issue 16, Pages 26226-26244Publisher
Optica Publishing Group
DOI: 10.1364/OE.492221
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This paper presents a method for improving the quality of noisy multi-channel imaging datasets by exploiting structural similarities between channels. 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. The authors demonstrate the applicability and performance of the method via three case studies.
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.& COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
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