Open AccessImproving principal component analysis based phase extraction method for phase-shifting interferometry by integrating spatial information
Author(s) -
Kohei Yatabe,
Kenji Ishikawa,
Yasuhiro Oikawa
Publication year - 2016
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.022881
Subject(s) - principal component analysis , singular value decomposition , computer science , phase (matter) , interferometry , phase retrieval , noise (video) , process (computing) , phase noise , matrix (chemical analysis) , optics , algorithm , pattern recognition (psychology) , artificial intelligence , fourier transform , mathematics , image (mathematics) , physics , materials science , mathematical analysis , quantum mechanics , composite material , operating system
Phase extraction methods based on the principal component analysis (PCA) can extract objective phase from phase-shifted fringes without any prior knowledge about their shift steps. Although it is fast and easy to implement, many fringe images are needed for extracting the phase accurately from noisy fringes. In this paper, a simple extension of the PCA method for reducing extraction error is proposed. It can effectively reduce influence from random noise, while most of the advantages of the PCA method is inherited because it only modifies the construction process of the data matrix from fringes. Although it takes more time because size of the data matrix to be decomposed is larger, computational time of the proposed method is shown to be reasonably fast by using the iterative singular value decomposition algorithm. Numerical experiments confirmed that the proposed method can reduce extraction error even when the number of interferograms is small.