Flenner, S., Bruns, S., Longo, E. et al. (4 more authors) (2022) Machine learning denoising of high-resolution X-ray nanotomography data. Journal of Synchrotron Radiation, 29 (1). pp. 230-238. ISSN 0909-0495
Abstract
High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Article available under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0). |
Keywords: | nanotomography; full-field X-ray microscopy; Zernike phase contrast; machine learning; denoising |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Jan 2022 12:21 |
Last Modified: | 11 Jan 2022 12:21 |
Status: | Published |
Publisher: | International Union of Crystallography (IUCr) |
Refereed: | Yes |
Identification Number: | 10.1107/s1600577521011139 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182280 |