Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature maps
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https://doi.org/10.1364/AO.511817 |
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Título
Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature mapsAutoría
Fecha de publicación
2024-05-10Editor
Optica Publishing GroupISSN
1559-128X; 2155-3165Cita bibliográfica
Shoma Kataoka, Yasuhiro Mizutani, Tsutomu Uenohara, Erick Ipus, Koichi Nitta, Osamu Matoba, Yasuhiro Takaya, and Enrique Tajahuerce, "Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature maps," Appl. Opt. 63, 3736-3744 (2024)Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://opg.optica.org/ao/fulltext.cfm?uri=ao-63-14-3736&id=549566Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Defect inspection is required in various fields, and many researchers have attempted deep-learning algorithms for inspections. Deep-learning algorithms have advantages in terms of accuracy and measurement time; however, ... [+]
Defect inspection is required in various fields, and many researchers have attempted deep-learning algorithms for inspections. Deep-learning algorithms have advantages in terms of accuracy and measurement time; however, the reliability of deep-learning outputs is problematic in precision measurements. This study demonstrates that iterative estimation using neighboring feature maps can evaluate the uncertainty of the outputs and shows that unconfident error predictions have higher uncertainties. In ghost imaging using deep learning, the experimental results show that removing outputs with higher uncertainties improves the accuracy by approximately 15.7%. [-]
Publicado en
Applied Optics, 2024, vol. 63, no 14Entidad financiadora
Japan Society for the Promotion of Science | Ministerio de Ciencia e Innovación | Generalitat Valenciana
Identificador de la entidad financiadora
http://dx.doi.org/10.13039/501100011033
Código del proyecto o subvención
JP19H02154 | JP20H05886 | JP22H01499 | JP23KJ1439 | JPJSBP120229927 | MCIN/PEICTI2021-2023/PID2022-142907OB-I00 | Prometeo/2020/029
Título del proyecto o subvención
Caracterización y control espaciotemporal de haces de luz y su aplicación a las ciencias de la vida
Derechos de acceso
© Optica Publishing Group
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
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