Quantitative Phase Imaging Using Deep Learning-Based Holographic Microscope

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.

Automated summary

Digital holographic microscopy combined with deep learning technology enables quantitative measurement of transparent specimens and investigation of biological samples, and has been applied in biological research. This paper proposes a neural network PhaseNet for the reconstruction of digital holograms and implements a deep learning-based holographic microscope, successfully measuring living mouse osteoblastic cells.