Machine learning holography for 3D particle field imaging

Siyao Shao, Kevin Mallery, S. Santosh Kumar, Jiarong Hong

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2987-2999
Number of pages13
JournalOptics Express
Volume28
Issue number3
DOIs
StatePublished - Feb 3 2020

Bibliographical note

Funding Information:
Office of Naval Research (N00014-16-1-2755). The authors would like to thank Prof. Xiang Cheng for access to the microscope used to generate the experimental training data.

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