Quantitative Phase Imaging Using Deep Learning-Based Holographic Microscope

Jianglei Di, Ji Wu, Kaiqiang Wang, Ju Tang, Ying Li, Jianlin Zhao

科研成果: 期刊稿件文章同行评审

23 引用 (Scopus)

摘要

Digital holographic microscopy enables the measurement of the quantitative light field information and the visualization of transparent specimens. It can be implemented for complex amplitude imaging and thus for the investigation of biological samples including tissues, dry mass, membrane fluctuation, etc. Currently, deep learning technologies are developing rapidly and have already been applied to various important tasks in the coherent imaging. In this paper, an optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital holograms, and a deep learning-based holographic microscope using above neural network is implemented for quantitative phase imaging. Living mouse osteoblastic cells are quantitatively measured to demonstrate the capability and applicability of the system.

源语言英语
文章编号651313
期刊Frontiers in Physics
9
DOI
出版状态已出版 - 22 3月 2021

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