Deep learning for faster holographic reconstruction processing in microfluidics

Daniele Pirone; Daniele Pirone; Daniele G. Sirico; Daniele G. Sirico; Lisa Miccio; Vittorio Bianco; Martina Mugnano; Pietro Ferraro; Pasquale Memmolo

doi:10.1364/DH.2022.Tu4A.7

Digital Holography and 3-D Imaging 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper Tu4A.7
•https://doi.org/10.1364/DH.2022.Tu4A.7

Deep learning for faster holographic reconstruction processing in microfluidics

Daniele Pirone, Daniele G. Sirico, Lisa Miccio, Vittorio Bianco, Martina Mugnano, Pietro Ferraro, and Pasquale Memmolo

Digital Holography and Three-Dimensional Imaging 2022

Cambridge United Kingdom
1–4 August 2022
ISBN: 978-1-957171-12-8

From the session
Phase Processing, Retrieval Techniques, and Applications I (Tu4A)

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D. Pirone, D. G. Sirico, L. Miccio, V. Bianco, M. Mugnano, P. Ferraro, and P. Memmolo, "Deep learning for faster holographic reconstruction processing in microfluidics," in Digital Holography and 3-D Imaging 2022, D. Chu, J. Park, C. Cheng, and P. Ferraro, eds., Technical Digest Series (Optica Publishing Group, 2022), paper Tu4A.7.

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Abstract

The huge amount of phase maps to be numerically retrieved from digital holograms is the actual bottleneck of the high-throughput holographic flow cytometry. An end-to-end neural network is discussed to speed up the holographic processing.

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