Publication
Title
Deep convolutional neural networks to restore single-shot electron microscopy images
Author
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.
Language
English
Source (journal)
N P J Computational Materials / Chinese academy of sciences. - Basingstoke
Publication
Basingstoke : Nature publishing group , 2024
ISSN
2057-3960
2057-3960
DOI
10.1038/S41524-023-01188-0
Volume/pages
10 :1 (2024) , p. 1-19
Article Reference
10
ISI
001138183000001
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Picometer metrology for light-element nanostructures: making every electron count (PICOMETRICS).
Quantifying the dynamics of the 3D atomic structure using hidden Markov models in scanning transmission electron microscopy.
Boosting properties and stability of metal halide nanocrystals and derived heterostructures by innovative transmission electron microscopy.
CHIral symmetry breaking from Surface to Bulk: a multidisciplinary approach of the crystallization of achiral and chiral molecules (CHISUB).
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 01.02.2024
Last edited 06.02.2024
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