Work

Phase retrieval of two random phase-shifting interferograms using Zernike coefficient extraction network

Year: 2022

Type: article

Abstract: This paper proposes a deep learning method for phase retrieval from two interferograms. The proposed method converts phase retrieval into the Zernike coefficient extraction problem, which can achieve Zernike coefficient extraction from two interferograms with random phase shifts. After knowing Zernike coefficients, the phase distribution can be retrieved using Zernike polynomials. The pre-filtering and phase unwrapping process are not required using the proposed method. The simulated data are analyzed, and the root mean square (RMS) of phase error reaches 0.01 λ. The effectiveness of the method is verified by the measured data.

Source: Optics Express

DOI: 10.1364/oe.470693

Authors: Ketao Yan, WenJun Yu, Congping Chen, Hongmin Zhang, Anand Asundi, Yingjie Yu

Institution: Shanghai University

Language: English

Topic: Optical measurement and interference techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: National Natural Science Foundation of China