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

TITLE=Quantitative Phase Imaging Using Deep Learning-Based Holographic Microscope

JOURNAL=Frontiers in Physics

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.651313

DOI=10.3389/fphy.2021.651313

ISSN=2296-424X

ABSTRACT=Digital holographic microscopy enables measurement of the quantitative light field information and the visualization of transparent specimens. It can be implemented for complex amplitude imaging and thus for investigation of biological samples including tissues, dry mass, membrane fluctuation, etc. Currently, deep learning technology is developing rapidly and has already been applied to various important tasks in coherent imaging. In this paper, an optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital hologram, 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.