Wu, L;
Bak, S;
Shin, Y;
Chu, YS;
Yoo, S;
Robinson, IK;
Huang, X;
(2023)
Resolution-enhanced X-ray fluorescence microscopy via deep residual networks.
npj Computational Materials
, 9
(1)
, Article 43. 10.1038/s41524-023-00995-9.
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Abstract
Multimodal hard X-ray scanning probe microscopy has been extensively used to study functional materials providing multiple contrast mechanisms. For instance, combining ptychography with X-ray fluorescence (XRF) microscopy reveals structural and chemical properties simultaneously. While ptychography can achieve diffraction-limited spatial resolution, the resolution of XRF is limited by the X-ray probe size. Here, we develop a machine learning (ML) model to overcome this problem by decoupling the impact of the X-ray probe from the XRF signal. The enhanced spatial resolution was observed for both simulated and experimental XRF data, showing superior performance over the state-of-the-art scanning XRF method with different nano-sized X-ray probes. Enhanced spatial resolutions were also observed for the accompanying XRF tomography reconstructions. Using this probe profile deconvolution with the proposed ML solution to enhance the spatial resolution of XRF microscopy will be broadly applicable across both functional materials and biological imaging with XRF and other related application areas.
| Type: | Article |
|---|---|
| Title: | Resolution-enhanced X-ray fluorescence microscopy via deep residual networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41524-023-00995-9 |
| Publisher version: | https://doi.org/10.1038/s41524-023-00995-9 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > London Centre for Nanotechnology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10167945 |
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