Wu, L;
Juhas, P;
Yoo, S;
Robinson, I;
(2021)
Complex imaging of phase domains by deep neural networks.
IUCrJ
, 8
(1)
10.1107/s2052252520013780.
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Abstract
The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data.
| Type: | Article |
|---|---|
| Title: | Complex imaging of phase domains by deep neural networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1107/s2052252520013780 |
| Publisher version: | https://doi.org/10.1107/s2052252520013780 |
| Language: | English |
| Additional information: | This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence (http://creativecommons.org/licenses/by/4.0/legalcode), which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
| Keywords: | machine learning; Bragg coherent X-ray diffraction; phase retrieval; single-particle imaging; deep neural networks |
| 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.ucl.ac.uk/id/eprint/10115748 |
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