%0 journal article
%@ 1600-5775
%A Flenner, S., Bruns, S., Longo, E., Parnell, A., Stockhausen, K., Müller, M., Greving, I.

%D 2022
%J Journal of Synchrotron Radiation
%N 1
%P 230-238 
%R doi:10.1107/S1600577521011139
%T Machine learning denoising of high-resolution X-ray nano­tomography data
%U https://doi.org/10.1107/S1600577521011139
1 
%X 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.