Machine Learning Holography for 3D Particle Field Imaging

We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate mea...

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Published in:arXiv.org 2019-11
Main Authors: Shao, Siyao, Mallery, Kevin, Kumar, Santosh, Hong, Jiarong
Format: Article
Language:English
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Holograms
Holography
Imaging
Machine learning
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Mallery, Kevin
Kumar, Santosh
Hong, Jiarong
description We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly-dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.
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subjects Holograms
Holography
Imaging
Machine learning
title Machine Learning Holography for 3D Particle Field Imaging
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