Fourier spectra for nonuniform phase-shifting algorithms based on principal component analysis
Manuel Servin, Moises Padilla, Guillermo Garnica, and Gonzalo Paez

TL;DR
This paper introduces a mathematically rigorous, error-corrected nonuniform phase-shifting algorithm based on PCA, with detailed analysis of its frequency transfer function, harmonic robustness, and SNR, improving phase demodulation accuracy.
Contribution
It presents a novel PCA-based nPSA with a comprehensive mathematical framework, including formulas for its FTF, correction methods, and performance metrics, addressing previous limitations.
Findings
PCA-nPSA can be modeled as a linear quadrature filter.
The FTF explains why plain PCA fails with nonuniform phase shifts.
The proposed formulas enable unbiased performance evaluation.
Abstract
We develop an error-free, nonuniform phase-stepping algorithm (nPSA) based on principal component analysis (PCA). PCA-based algorithms typically give phase-demodulation errors when applied to nonuniform phase-shifted interferograms. We present a straightforward way to correct those PCA phase-demodulation errors. We give mathematical formulas to fully analyze PCA-based nPSA (PCA-nPSA). These formulas give a) the PCA-nPSA frequency transfer function (FTF), b) its corrected Lissajous figure, c) the corrected PCA-nPSA formula, d) its harmonic robustness, and e) its signal-to-noise-ratio (SNR). We show that the PCA-nPSA can be seen as a linear quadrature filter, and as consequence, one can find its FTF. Using the FTF, we show why plain PCA often fails to demodulate nonuniform phase-shifted fringes. Previous works on PCA-nPSA (without FTF), give specific numerical/experimental fringe data to…
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