Cargando…

Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework

The exit wave is the state of a uniform plane incident electron wave exiting immediately after passing through a specimen and before the atomic-resolution transmission electron microscopy (ARTEM) image is modified by the aberration of the optical system and the incoherence effect of the electron. Al...

Descripción completa

Detalles Bibliográficos
Autores principales: Lee, Jongyeong, Lee, Yeongdong, Kim, Jaemin, Lee, Zonghoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601262/
https://www.ncbi.nlm.nih.gov/pubmed/33036252
http://dx.doi.org/10.3390/nano10101977
_version_ 1783603367641088000
author Lee, Jongyeong
Lee, Yeongdong
Kim, Jaemin
Lee, Zonghoon
author_facet Lee, Jongyeong
Lee, Yeongdong
Kim, Jaemin
Lee, Zonghoon
author_sort Lee, Jongyeong
collection PubMed
description The exit wave is the state of a uniform plane incident electron wave exiting immediately after passing through a specimen and before the atomic-resolution transmission electron microscopy (ARTEM) image is modified by the aberration of the optical system and the incoherence effect of the electron. Although exit-wave reconstruction has been developed to prevent the misinterpretation of ARTEM images, there have been limitations in the use of conventional exit-wave reconstruction in ARTEM studies of the structure and dynamics of two-dimensional materials. In this study, we propose a framework that consists of the convolutional dual-decoder autoencoder to reconstruct the exit wave and denoise ARTEM images. We calculated the contrast transfer function (CTF) for real ARTEM and assigned the output of each decoder to the CTF as the amplitude and phase of the exit wave. We present exit-wave reconstruction experiments with ARTEM images of monolayer graphene and compare the findings with those of a simulated exit wave. Cu single atom substitution in monolayer graphene was, for the first time, directly identified through exit-wave reconstruction experiments. Our exit-wave reconstruction experiments show that the performance of the denoising task is improved when compared to the Wiener filter in terms of the signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index map metrics.
format Online
Article
Text
id pubmed-7601262
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-76012622020-11-01 Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework Lee, Jongyeong Lee, Yeongdong Kim, Jaemin Lee, Zonghoon Nanomaterials (Basel) Article The exit wave is the state of a uniform plane incident electron wave exiting immediately after passing through a specimen and before the atomic-resolution transmission electron microscopy (ARTEM) image is modified by the aberration of the optical system and the incoherence effect of the electron. Although exit-wave reconstruction has been developed to prevent the misinterpretation of ARTEM images, there have been limitations in the use of conventional exit-wave reconstruction in ARTEM studies of the structure and dynamics of two-dimensional materials. In this study, we propose a framework that consists of the convolutional dual-decoder autoencoder to reconstruct the exit wave and denoise ARTEM images. We calculated the contrast transfer function (CTF) for real ARTEM and assigned the output of each decoder to the CTF as the amplitude and phase of the exit wave. We present exit-wave reconstruction experiments with ARTEM images of monolayer graphene and compare the findings with those of a simulated exit wave. Cu single atom substitution in monolayer graphene was, for the first time, directly identified through exit-wave reconstruction experiments. Our exit-wave reconstruction experiments show that the performance of the denoising task is improved when compared to the Wiener filter in terms of the signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index map metrics. MDPI 2020-10-06 /pmc/articles/PMC7601262/ /pubmed/33036252 http://dx.doi.org/10.3390/nano10101977 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Jongyeong
Lee, Yeongdong
Kim, Jaemin
Lee, Zonghoon
Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework
title Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework
title_full Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework
title_fullStr Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework
title_full_unstemmed Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework
title_short Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework
title_sort contrast transfer function-based exit-wave reconstruction and denoising of atomic-resolution transmission electron microscopy images of graphene and cu single atom substitutions by deep learning framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601262/
https://www.ncbi.nlm.nih.gov/pubmed/33036252
http://dx.doi.org/10.3390/nano10101977
work_keys_str_mv AT leejongyeong contrasttransferfunctionbasedexitwavereconstructionanddenoisingofatomicresolutiontransmissionelectronmicroscopyimagesofgrapheneandcusingleatomsubstitutionsbydeeplearningframework
AT leeyeongdong contrasttransferfunctionbasedexitwavereconstructionanddenoisingofatomicresolutiontransmissionelectronmicroscopyimagesofgrapheneandcusingleatomsubstitutionsbydeeplearningframework
AT kimjaemin contrasttransferfunctionbasedexitwavereconstructionanddenoisingofatomicresolutiontransmissionelectronmicroscopyimagesofgrapheneandcusingleatomsubstitutionsbydeeplearningframework
AT leezonghoon contrasttransferfunctionbasedexitwavereconstructionanddenoisingofatomicresolutiontransmissionelectronmicroscopyimagesofgrapheneandcusingleatomsubstitutionsbydeeplearningframework