Denoising atomic resolution 4D scanning transmission electron microscopy data with tensor singular value decomposition
Abstract
Tensor singular value decomposition (SVD) is a method to find a low-dimensional representation of data with meaningful structure in three or more dimensions. Here, tensor SVD has been applied to denoise atomic-resolution 4D scanning transmission electron microscopy (4D STEM) data. On data simulated from a SrTiO3 [100] perfect crystal and a Si [110] edge dislocation, tensor SVD achieved an average peak signal-to-noise ratio (PSNR) of ~40 dB, which matches or exceeds the performance of other denoising methods, with processing times at least 100 times shorter. On experimental data from SrTiO3 [100] and LiZnSb [11 0]/GaSb [110] samples, tensor SVD denoises multiple GB 4D STEM data sets in ten minutes on a typical personal computer. Denoising with tensor SVD improves both convergent beam electron diffraction patterns and virtual-aperture annular dark field images.
- Authors:
-
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Materials Science and Engineering
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Statistics
- Publication Date:
- Research Org.:
- Univ. of Wisconsin, Madison, WI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); National Institutes of Health (NIH)
- OSTI Identifier:
- 1849893
- Alternate Identifier(s):
- OSTI ID: 1809538
- Grant/Contract Number:
- FG02-08ER46547; DMS-1811868; CAREER-1944904; DMR-1720415; R01-GM131399
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Ultramicroscopy
- Additional Journal Information:
- Journal Volume: 219; Journal Issue: C; Journal ID: ISSN 0304-3991
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; 36 MATERIALS SCIENCE; microscopy; scanning transmission electron microscopy; convergent beam electron diffraction; image denoising; low-rank tensor
Citation Formats
Zhang, Chenyu, Han, Rungang, Zhang, Anru R., and Voyles, Paul. M. Denoising atomic resolution 4D scanning transmission electron microscopy data with tensor singular value decomposition. United States: N. p., 2020.
Web. doi:10.1016/j.ultramic.2020.113123.
Zhang, Chenyu, Han, Rungang, Zhang, Anru R., & Voyles, Paul. M. Denoising atomic resolution 4D scanning transmission electron microscopy data with tensor singular value decomposition. United States. https://doi.org/10.1016/j.ultramic.2020.113123
Zhang, Chenyu, Han, Rungang, Zhang, Anru R., and Voyles, Paul. M. Mon .
"Denoising atomic resolution 4D scanning transmission electron microscopy data with tensor singular value decomposition". United States. https://doi.org/10.1016/j.ultramic.2020.113123. https://www.osti.gov/servlets/purl/1849893.
@article{osti_1849893,
title = {Denoising atomic resolution 4D scanning transmission electron microscopy data with tensor singular value decomposition},
author = {Zhang, Chenyu and Han, Rungang and Zhang, Anru R. and Voyles, Paul. M.},
abstractNote = {Tensor singular value decomposition (SVD) is a method to find a low-dimensional representation of data with meaningful structure in three or more dimensions. Here, tensor SVD has been applied to denoise atomic-resolution 4D scanning transmission electron microscopy (4D STEM) data. On data simulated from a SrTiO3 [100] perfect crystal and a Si [110] edge dislocation, tensor SVD achieved an average peak signal-to-noise ratio (PSNR) of ~40 dB, which matches or exceeds the performance of other denoising methods, with processing times at least 100 times shorter. On experimental data from SrTiO3 [100] and LiZnSb [112¯0]/GaSb [110] samples, tensor SVD denoises multiple GB 4D STEM data sets in ten minutes on a typical personal computer. Denoising with tensor SVD improves both convergent beam electron diffraction patterns and virtual-aperture annular dark field images.},
doi = {10.1016/j.ultramic.2020.113123},
journal = {Ultramicroscopy},
number = C,
volume = 219,
place = {United States},
year = {Mon Oct 05 00:00:00 EDT 2020},
month = {Mon Oct 05 00:00:00 EDT 2020}
}
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