Affiliation:
1. Heidelberg University
2. The University of Manchester
3. Karlsruhe Institute of Technology
4. STFC
5. Diamond Light Source
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.
Funder
Bundesministerium für Bildung und Forschung
Subject
Atomic and Molecular Physics, and Optics
Cited by
1 articles.
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