| Issue |
EPJ Web Conf.
Volume 309, 2024
EOS Annual Meeting (EOSAM 2024)
|
|
|---|---|---|
| Article Number | 15005 | |
| Number of page(s) | 2 | |
| Section | Focused Sessions (FS) 5- Machine-Learning for Optics and Photonic Computing for AI | |
| DOI | https://doi.org/10.1051/epjconf/202430915005 | |
| Published online | 31 October 2024 | |
https://doi.org/10.1051/epjconf/202430915005
Physics-driven learning for digital holographic microscopy
Université de Franche-Comté, CNRS, FEMTO-ST Institute, 25000 Besançon, France
* e-mail: maxime.jacquot@univ-fcomte.fr
Published online: 31 October 2024
Deep neural networks based on physics-driven learning make it possible to train neural networks with a reduced data set and also have the potential to transfer part of the numerical computations to optical processing. The aim of this work is to develop the first deep holographic microscope device incorporating a hybrid neural network based on the plane-wave angular spectrum method for dynamic image autofocusing in microscopy applications.
© The Authors, published by EDP Sciences, 2024
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