High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples

TL;DR

This paper introduces a deep neural network-based method for high-resolution, single-shot phase imaging in microscopy, enabling accurate quantitative phase measurements from just one interferogram, which enhances biomedical imaging capabilities.

Contribution

It presents a novel deep learning approach integrated with white light phase-shifting microscopy for single-shot, high-resolution phase retrieval in biological samples, reducing the need for multiple interferograms.

Findings

Deep neural network accurately generates phase-shifted frames from a single interferogram.

The method produces phase maps comparable to those from traditional multi-frame techniques.

Validation on biological samples demonstrates potential for biomedical applications.

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.