@misc{flenner_machine_learning_2022, 
author={Flenner, S., Bruns, S., Longo, E., Parnell, A., Stockhausen, K., Müller, M., Greving, I.},
title={Machine learning denoising of high-resolution X-ray nano­tomography data},
year={2022},
howpublished = {journal article},
doi = {https://doi.org/10.1107/S1600577521011139},
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.},
note = {Online available at: \url{https://doi.org/10.1107/S1600577521011139} (DOI). Flenner, S.; Bruns, S.; Longo, E.; Parnell, A.; Stockhausen, K.; Müller, M.; Greving, I.: Machine learning denoising of high-resolution X-ray nano­tomography data. Journal of Synchrotron Radiation. 2022. vol. 29, no. 1, 230-238. DOI: 10.1107/S1600577521011139}}