ArticleScientific reports2021
Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 21 citations in OpenAlex.
- Domain-specific simulated data enhances knife-mark noise suppression in microscopy images of materials.Microscopy (Oxford, England) · 2026Article
- In-situ heating-and-electron tomography for materials research: from 3D (in-situ 2D) to 4D (in-situ 3D).Microscopy (Oxford, England) · 2024Article
- Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.Scientific reports · 2022Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.
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Registered trials
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