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用于相位重建的先进主成分分析方法。

Advanced principal component analysis method for phase reconstruction.

作者信息

Deng Jian, Wang Kai, Wu Dan, Lv Xiaoxu, Li Chen, Hao Junjie, Qin Jing, Chen Wei

出版信息

Opt Express. 2015 May 4;23(9):12222-31. doi: 10.1364/OE.23.012222.

DOI:10.1364/OE.23.012222
PMID:25969308
Abstract

Focus on the phase reconstruction from three phase-shifting interferograms with unknown phase shifts, an advanced principal component analysis method is proposed. First, use a simple subtraction operation among interferograms, two intensity difference images are obtained easily. Second, set the center region of the data of intensity difference images to zero, and then construct a covariance matrix to obtain a transformation matrix. Third, two principal components of interferograms can be determined by the Hotelling transform and then phase can be calculated from the two normalized principal components by an arctangent function. By means of the simulation calculation and the experimental research, it is proved that the phase with high precision can be obtained rapidly by the proposed algorithm.

摘要

针对从具有未知相移的三幅相移干涉图进行相位重建的问题,提出了一种改进的主成分分析方法。首先,在干涉图之间进行简单的减法运算,很容易得到两幅强度差图像。其次,将强度差图像数据的中心区域设为零,然后构造协方差矩阵以获得变换矩阵。第三,通过霍特林变换确定干涉图的两个主成分,然后通过反正切函数从这两个归一化主成分计算相位。通过模拟计算和实验研究,证明了所提算法能够快速获得高精度的相位。

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引用本文的文献

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Random two-frame interferometry based on deep learning.基于深度学习的随机双帧干涉测量法。
Opt Express. 2020 Aug 17;28(17):24747-24760. doi: 10.1364/OE.397904.
2
Precise phase retrieval under harsh conditions by constructing new connected interferograms.通过构建新的连通干涉图在恶劣条件下进行精确的相位恢复。
Sci Rep. 2016 Apr 14;6:24416. doi: 10.1038/srep24416.