微流体中更快全息重建处理的深度学习

Digital Holography and 3-D Imaging 2022 Pub Date : 1900-01-01 DOI:10.1364/dh.2022.tu4a.7

D. Pirone, Daniele G Sirico, L. Miccio, V. Bianco, M. Mugnano, P. Ferraro, P. Memmolo

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引用次数: 0

摘要

从数字全息图中提取的大量相位图是高通量全息流式细胞术的实际瓶颈。讨论了一种端到端神经网络来提高全息处理的速度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep learning for faster holographic reconstruction processing in microfluidics

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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Digital Holography and 3-D Imaging 2022

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