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Author |
Lobato, I.; Friedrich, T.; Van Aert, S. |
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Title |
Deep convolutional neural networks to restore single-shot electron microscopy images |
Type |
A1 Journal Article |
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Year  |
2024 |
Publication |
npj Computational Materials |
Abbreviated Journal |
npj Comput Mater |
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Volume |
10 |
Issue |
1 |
Pages |
10 |
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Keywords |
A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ; |
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Abstract |
Advanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology. |
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Wos |
001138183000001 |
Publication Date |
2024-01-09 |
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ISSN |
2057-3960 |
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Additional Links |
UA library record; WoS full record |
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Notes |
This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A.). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N, G0A7723N and EOS 40007495). S.V.A. acknowledges funding from the University of Antwerp Research Fund (BOF). The authors thank Lukas Grünewald for data acquisition and support for Fig. 7. |
Approved |
Most recent IF: NA |
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Call Number |
EMAT @ emat @c:irua:202714 |
Serial |
8994 |
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Author |
De Backer, A.; Van Aert, S.; Faes, C.; Arslan Irmak, E.; Nellist, P.D.; Jones, L. |
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Title |
Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
N P J Computational Materials |
Abbreviated Journal |
npj Comput Mater |
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Volume |
8 |
Issue |
1 |
Pages |
216 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses. |
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Wos |
000866500900001 |
Publication Date |
2022-10-12 |
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Abbreviated Series Title |
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Series Volume |
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Edition |
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ISSN |
2057-3960 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
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Times cited |
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Open Access |
OpenAccess |
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Notes |
This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A. and Grant 823717 ESTEEM3). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G.0267.18N, G.0502.18N, G.0346.21N) and a postdoctoral grant to A.D.B. L.J. acknowledges Science Foundation Ireland (SFI – grant number URF/RI/191637), the Royal Society, and the AMBER Centre. The authors acknowledge Aakash Varambhia for his assistance and expertise with the experimental recording and use of characterization facilities within the David Cockayne Centre for Electron Microscopy, Department of Materials, University of Oxford, and in particular the EPSRC (EP/K040375/1 South of England Analytical Electron Microscope).; esteem3reported; esteem3JRA |
Approved |
Most recent IF: NA |
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Call Number |
EMAT @ emat @c:irua:191398 |
Serial |
7114 |
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