Quantitative Phase Imaging Using Deep Learning-Based Holographic Microscope

Frontiers in Physics

Ying Li¹  Ji Wu¹  Kaiqiang Wang¹  Ju Tang¹  Jianglei Di²  Jianlin Zhao³ 

[1] Xi’an, China;[e-mail];null;

关键词: digital holographic microscopy;    digital holography;    deep learning;    quantitative phase imaging;    convolution neural network;   

DOI  :  10.3389/fphy.2021.651313

来源: Frontiers

【 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.

【 授权许可】

CC BY