Demodulation of a single closed-fringe interferogram with symmetric wavefront and tilt

Muñoz-Maciel, Jesús; Durán-Ramı́rez, Victor M.; Mora-González, Miguel; Casillas-Rodríguez, Francisco J.; Peña-Lecona, Francisco G.

doi:10.1016/j.optcom.2018.12.019

Cited by 9 publications

(1 citation statement)

References 14 publications

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“…Sci. 2023, 13, 11969 2 of 12 use different computational strategies, such as the combination of genetic algorithms and parametric methods [3][4][5]; soft computing techniques applied to Zernike polynomials [6]; a combination of genetic algorithms and frequency-guided sequential demodulation [7]; combined harmony search optimization and frequency-guided sequential demodulation [1]; demodulate directly with Particle Swarm Optimization [8]; unwrapping of phase maps with sign changes [9]; demodulation with symmetric wavefront and tilt [10]; two-dimensional regularized phase-tracking technique [11]; different techniques of deep learning [12,13]; convolutional neural network [14]; and Lissajous ellipse fitting [15], among others.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Frequency-Guided Method with Particle Swarm Optimization for the Phase Recovery of a Single Interferogram

Quintanar-Sotelo,

Casillas-Rodriguez,

Muñoz-Maciel

et al. 2023

Applied Sciences

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Frequency-guided sequential demodulation (FSD) is a technique used to recover the phase of closed fringe patterns when only a single interferogram is available. FSD is a sequential method that follows simple and intuitive processing steps for interferogram demodulation, guided by the frequency of the fringes. The objective of this work is to apply an optimizer block that feeds from the first stages of the FSD and make feedback in such a manner as to optimize the search for guided frequencies. This approach significantly reduces demodulation errors, especially when dealing with high-frequency fringe patterns. The results of using the FSD with and without optimizers are compared. The optimizers used are the Harmonic Search Optimization (HSO) as well as the Particle Swarm Optimization (PSO). Results show that FSD with PSO delivers better results compared with other methods here analyzed.

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