Machine learning denoising of high-resolution X-ray nanotomography data
open access.
| Veröffentlicht in: | Journal of synchrotron radiation. - 1994. - 29(2022), Pt 1 vom: 01. Jan., Seite 230-238 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Journal of synchrotron radiation |
| Schlagworte: | Journal Article Zernike phase contrast denoising full-field X-ray microscopy machine learning nanotomography |
| Zusammenfassung: | open access. 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 |
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| Beschreibung: | Date Revised 05.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1600-5775 |
| DOI: | 10.1107/S1600577521011139 |