Machine learning denoising of high‐resolution X‐ray nanotomography data. (21st December 2021)

• Record Type: Journal Article
• Authors: Flenner, Silja; Bruns, Stefan; Longo, Elena; Parnell, Andrew J.; Stockhausen, Kilian E.; Müller, Martin; Greving, Imke
• Is Part Of: Journal of synchrotron radiation. Volume 29:Part 1(2022)
• Page Start: 230
• Page End: 238
• Publication Date: 2021-12-21
• Subjects: nanotomography -- full‐field X‐ray microscopy -- Zernike phase contrast -- machine learning -- denoising; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577521011139
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : A high‐performance denoising filter based on machine learning for high‐resolution synchrotron nanotomography data is analyzed and evaluated. 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.