Generalization of learned Fourier-based phase-diversity wavefront sensing

Zhisheng Zhou, Qiang Fu, Jingang Zhang, Yunfeng Nie

Research output: Contribution to journalArticlepeer-review


Proper initialization of the nonlinear optimization is important to avoid local minima in phase diversity wavefront sensing (PDWS). An effective neural network based on low-frequency coefficients in the Fourier domain has proved effective to determine a better estimate of the unknown aberrations. However, the network relies significantly on the training settings, such as imaging object and optical system parameters, resulting in a weak generalization ability. Here we propose a generalized Fourier-based PDWS method by combining an object-independent network with a system-independent image processing procedure. We demonstrate that a network trained with a specific setting can be applied to any image regardless of the actual settings. Experimental results show that a network trained with one setting can be applied to images with four other settings. For 1000 aberrations with RMS wavefront errors bounded within [0.2 λ, 0.4 λ], the mean RMS residual errors are 0.032 λ, 0.039 λ, 0.035 λ, and 0.037 λ, respectively, and 98.9% of the RMS residual errors are less than 0.05 λ.
Original languageEnglish (US)
Pages (from-to)11729
JournalOptics Express
Issue number7
StatePublished - Mar 23 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-03-29
Acknowledgements: Beijing Municipal Natural Science Foundation (JQ22029); Informatization Plan of Chinese Academy of Sciences (CAS-WX2021-PY-0110); Shenzhen Public Technical Service Platform program (GGFW2018020618063670); Fonds Wetenschappelijk Onderzoek (1252722N); Equipment Research Program of the Chinese Academy of Sciences (Y70X25A1HY, YJKYYQ20180039).

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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