ArticleJournal of synchrotron radiation2022
Machine learning denoising of high-resolution X-ray nanotomography data.
Article in Journal of synchrotron radiation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- HoToPy: a toolbox for X-ray holo-tomography in Python.Journal of synchrotron radiation · 2025Article
- Development of a deep learning method for phase retrieval image enhancement in phase contrast microcomputed tomography.Journal of microscopy · 2025Article
- Scaling up X-ray holographic nanotomography for neuronal tissue imaging.Biomedical optics express · 2025Article
- Self-supervised denoising of grating-based phase-contrast computed tomography.Scientific reports · 2024Article
- Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging.Journal of synchrotron radiation · 2024Article
- Dual-beam X-ray nano-holotomography.Journal of synchrotron radiation · 2024Article
- Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone.Journal of synchrotron radiation · 2024Article
- On the material dependency of peri-implant morphology and stability in healing bone.Bioactive materials · 2023Article
- Hard X-ray full-field nanoimaging using a direct photon-counting detector.Journal of synchrotron radiation · 2023Article
- Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.IS&T International Symposium on Electronic Imaging · 2023Article
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Authors and funding
7 authors.
Funding
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.
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