Deep Phase Shifter for Quantitative Phase Imaging

TL;DR

This paper introduces a deep learning-based digital phase shifter that enables high-accuracy, full-field quantitative phase imaging from a single hologram, overcoming limitations of traditional holography methods.

Contribution

It presents a novel deep-phase-shift network (DPS-net) that replaces physical phase shifters, allowing arbitrary phase shift generation from a single interferogram in in-line holography.

Findings

DPS-net can generate multiple phase-shifted interferograms from one original.

The method achieves high accuracy in phase shift generation.

Experimental results confirm the effectiveness of the proposed approach.

Abstract

A single intensity-only holographic interferogram can records the full amplitude and phase information of optical field. However, current digital holography technologies cannot recover the lossless phase information from a single interferogram. In this paper, we provide an entirely new approach for the full-field quantitative phase imaging technology. We demonstrate that deep learning can be used to replace the entitative phase shifter, and quantitative phase imaging can obtain quantitative phase from a single interferogram in in-line holography. A deep-phase-shift network (DPS-net) is reported, which can be trained with simulation training data. The trained DPS-net can be used to generate multiple interferograms with arbitrary phase shift from a single interferogram as an artificial intelligence phase shifter. The ability and the accuracy of generating arbitrary phase shifts are verified, and the performance of the proposed method is also verified by the experimental interferogram. The results demonstrate that the proposed method can provide a full digital phase shifter with high-accuracy for the technology of dynamic quantitative phase measurement.