Fast reconstruction of scanning transmission electron microscopy images using Markov random field model.
Bayesian inference
Denoising
Markov random field model
Scanning transmission electron microscopy
Journal
Ultramicroscopy
ISSN: 1879-2723
Titre abrégé: Ultramicroscopy
Pays: Netherlands
ID NLM: 7513702
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
22
05
2023
accepted:
06
07
2023
medline:
28
7
2023
pubmed:
28
7
2023
entrez:
27
7
2023
Statut:
ppublish
Résumé
In this study, we proposed a fast method of reconstruction for scanning transmission electron microscopy images. The proposed method is based on the Markov random field model and Bayesian inference, and we found that the method can reconstruct such images of sizes 512 × 512 and 264 × 240 in less than 200 ms and 100 ms, respectively. Furthermore, we showed that the method of reconstruction from multiple images without averaging them has better reconstruction performance than that from the averaged image.
Identifiants
pubmed: 37499573
pii: S0304-3991(23)00128-6
doi: 10.1016/j.ultramic.2023.113811
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
113811Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Masato Okada, Naoya Shibata, Ryo Ishikawa reports financial support was provided by Government of Japan Ministry of Education Culture Sports Science and Technology. Masato Okada, Shun Katakami, Taichi Kusumi, Naoya Shibata, Ryo Ishikawa, Kazuki Kawahara has patent pending to The University of Tokyo.