Robust statistical phase-diversity method for high-accuracy wavefront sensing

Zhisheng Zhou, Yunfeng Nie, Qiang Fu, Qiran Liu, Jingang Zhang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Phase diversity phase retrieval (PDPR) has been a popular technique for quantitatively measuring wavefront errors of optical imaging systems by extracting the phase information from several designated intensity measurements. As the problem is inverse and non-convex in general, the accuracy and robustness of most such algorithms rely greatly on the initial conditions. In this work, we propose a new strategy that combines Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) with the initial points generated by k-means clustering method and three various channels to improve the overall performance. Experimental results show that, for 500 different phase aberrations with root mean square (RMS) value bounded within [0.2λ, 0.3λ], the minimum, the maximum and the mean RMS residual errors reach 0.017λ, 0.066λ and 0.039λ, respectively, and 84.8% of the RMS residual errors are less than 0.05λ. We have further investigated and analyzed the proposed method in details to quantitatively demonstrate its performance: statistical results reveal that our proposed PDPR with k-means clustering enhanced method has excellent robustness in terms of initial points and other influential factors, and the accuracy can outperform its counterpart methods such as classic L-BFGS and modified BFGS.
Original languageEnglish (US)
Pages (from-to)106335
JournalOptics and Lasers in Engineering
Volume137
DOIs
StatePublished - Aug 28 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Equipment Research Program of the Chinese Academy of Sciences (no. YJKYYQ20180039 and no. Y70X25A1HY), and the National Natural Science Foundation of China (no. 61775219, no. 61771369 and no. 61640422).

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