Obtaining the phase of an interferogram by use of an evolution strategy: Part I

Vázquez‐Montiel, S.; Sánchez-Escobar, Juan Jaime; Fuentes, Olac

doi:10.1364/ao.41.003448

Cited by 16 publications

(16 citation statements)

References 6 publications

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“…On average, each interferogram took 1.6 seconds to process, which is much faster than the results reported in recent works dealing with the same problem. For example, [11] reports that evolution strategies took about 3 minutes to find the parameters of each interferogram, using the same resolution and similar computing hardware. Real data always pose the challenge of managing noise.…”

Section: Resultsmentioning

confidence: 99%

“…This is a difficult problem, and traditional optimization schemes based on the least-squares method often provide inconclusive results, specially in the presence of noisy data [12,13]. For this reason, nontraditional optimization schemes, such as evolutionary algorithms, have been proposed to solve this problem [11]. While evolutionary algorithms provide very accurate results in the case of both noiseless and noisy data, their running time is high, taking several minutes to analyze a single interferogram.…”

Section: Interferometrymentioning

confidence: 99%

“…This is an interesting domain, as there are published attempted solutions using both traditional optimization schemes and evolutionary algorithms [10,11].…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Algorithm Based on Active and Instance-Based Learning

Fuentes

Solorio

2004

MICAI 2004: Advances in Artificial Intelligence

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Abstract. We present an optimization algorithm that combines active learning and locally-weighted regression to find extreme points of noisy and complex functions. We apply our algorithm to the problem of interferogram analysis, an important problem in optical engineering that is not solvable using traditional optimization schemes and that has received recent attention in the research community. Experimental results show that our method is faster than others previously presented in the literature and that it is very accurate for the case of noiseless interferograms, as well as for the case of interferograms with two types of noise: white noise and intensity gradients, which are due to slight missalignments in the system.

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Section: Resultsmentioning

confidence: 99%

Section: Interferometrymentioning

confidence: 99%

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An Optimization Algorithm Based on Active and Instance-Based Learning

Fuentes

Solorio

2004

MICAI 2004: Advances in Artificial Intelligence

Self Cite

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“…Recently, the analyses of interferograms using techniques, such as genetic algorithms, 23,24 evolutive strategy, 25,26 and probabilistic estimation, 19 have been investigated. These techniques use methods to compare images that are generated synthetically with images obtained experimentally, to estimate, with a high precision, the value of the phase.…”

Section: Introductionmentioning

confidence: 99%

Study of the factors that affect the correlation behavior during the evaluation of interferograms

2018

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In recent years, the correlation coefficient has been used as a tool for comparison between experimental and synthetic interferograms in algorithms of evaluation, in the areas of optical fabrication and testing. This coefficient has been used with the aim of eliminating the observer criterion during the fabrication process, and in this sense, to make a quantitative test to compare experimental and synthetic interferograms using the correlation as a parameter of evaluation. However, this coefficient is dramatically affected when laboratory conditions are not adequate. Therefore, in this work, we present a detailed analysis of the correlation behavior when interferograms with different values of visibility, Gaussian noise, and background illumination are evaluated/correlated. To analyze the correlation behavior, we simulated different interferograms, where these parameters were varied and examined how they affect the interference patterns. We found that a bad illumination dramatically affects the value of correlation, causing it to decrease to 0.1046, with σ ¼ 0.1.

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“…8 In this latter case, a numerical 8 or analytical integration 9 can be applied to estimate the OPD function. To evaluate phase function from a pattern of fringes, 10 the correlation coefficient between the experimental and simulated interferogram is maximized by using genetic algorithms. Recently, a similar procedure has been used to evaluate Ronchigrams.…”

Section: Introductionmentioning

confidence: 99%

Optical surface evaluation by correlating bi-Ronchigram images

Cordero-Dávila

González-García

2015

Opt. Eng

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Abstract. By correlating bi-Ronchigram images, surface errors without any supposed symmetry are evaluated. No approximation and interference orders are needed. First, only one experimental bi-Ronchigram (Ronchigram with a square grid) image was recorded. Second, given a surface parameter set (curvature radius, conic constant, and/or symmetric and asymmetric deformation coefficients), a bi-Ronchigram image is simulated and correlated with the bi-Ronchigram experimental image. Third, genetic algorithms are used to find the parameters for which the correlation coefficient reaches its maximum value. Finally, the parameters of the experimental surface are estimated. Evaluations of curvature radius, conic constant, and error functions for reflecting surfaces will be shown. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Obtaining the phase of an interferogram by use of an evolution strategy: Part I

Cited by 16 publications

References 6 publications

An Optimization Algorithm Based on Active and Instance-Based Learning

An Optimization Algorithm Based on Active and Instance-Based Learning

Study of the factors that affect the correlation behavior during the evaluation of interferograms

Optical surface evaluation by correlating bi-Ronchigram images

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