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 |
Metadades
Títol
Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature mapsAutoria
Data de publicació
2024-05-10Editor
Optica Publishing GroupISSN
1559-128X; 2155-3165Cita bibliogràfica
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)Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://opg.optica.org/ao/fulltext.cfm?uri=ao-63-14-3736&id=549566Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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%. [-]
Publicat a
Applied Optics, 2024, vol. 63, no 14Entitat finançadora
Japan Society for the Promotion of Science | Ministerio de Ciencia e Innovación | Generalitat Valenciana
Identificador de l'entitat finançadora
http://dx.doi.org/10.13039/501100011033
Codi del projecte o subvenció
JP19H02154 | JP20H05886 | JP22H01499 | JP23KJ1439 | JPJSBP120229927 | MCIN/PEICTI2021-2023/PID2022-142907OB-I00 | Prometeo/2020/029
Títol del projecte o subvenció
Caracterización y control espaciotemporal de haces de luz y su aplicación a las ciencias de la vida
Drets d'accés
© 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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