Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and...

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Vydané v:	Small (Weinheim an der Bergstrasse, Germany) Ročník 21; číslo 33; s. e2503019 - n/a
Hlavní autori:	Sultanov, Seyfal, Ayyubi, R. A. W., Buban, James P., Klie, Robert F.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Germany Wiley Subscription Services, Inc 01.08.2025 John Wiley and Sons Inc
Predmet:	Anomalies anomaly detection Automation convolutional variational autoencoders Defects Electron energy electron energy loss spectroscopy Image reconstruction Performance evaluation Principal components analysis Robustness Spectra spectral anomalies spectral imaging Spectrum analysis convolutional variational autoencoders spectral imaging anomaly detection spectral anomalies electron energy loss spectroscopy
ISSN:	1613-6810, 1613-6829, 1613-6829
On-line prístup:	Získať plný text
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