AUTHOR=Wang Wenjian , Ali Nauman , Ma Ying , Dong Zhao , Zuo Chao , Gao Peng TITLE=Deep learning-based quantitative phase microscopy JOURNAL=Frontiers in Physics VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1218147 DOI=10.3389/fphy.2023.1218147 ISSN=2296-424X ABSTRACT=

Quantitative phase microscopy (QPM) is a powerful tool for label-free and noninvasive imaging of transparent specimens. In this paper, we propose a novel QPM approach that utilizes deep learning to reconstruct accurately the phase image of transparent specimens from a defocus bright-field image. A U-net based model is used to learn the mapping relation from the defocus intensity image to the phase distribution of a sample. Both the off-axis hologram and defocused bright-field image are recorded in pair for thousands of virtual samples generated by using a spatial light modulator. After the network is trained with the above data set, the network can fast and accurately reconstruct the phase information through a defocus bright-field intensity image. We envisage that this method will be widely applied in life science and industrial detection.