High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples
Abstract
White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate 1) four phase-shifted frames and 2) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.
Cite
@article{arxiv.2010.07768,
title = {High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples},
author = {Sunil Bhatt and Ankit Butola and Sheetal Raosaheb Kanade and Anand Kumar and Dalip Singh Mehta},
journal= {arXiv preprint arXiv:2010.07768},
year = {2021}
}