Open AccessMachine learning denoising of high‐resolution X‐ray nanotomography data
Author(s) -
Flenner Silja,
Bruns Stefan,
Longo Elena,
Parnell Andrew J.,
Stockhausen Kilian E.,
Müller Martin,
Greving Imke
Publication year - 2022
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s1600577521011139
Subject(s) - noise reduction , filter (signal processing) , noise (video) , computer science , artificial intelligence , resolution (logic) , segmentation , pattern recognition (psychology) , computer vision , image resolution , image (mathematics)
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.