Algorithm for Ronchigram recovery with random aberrations coefficients

Aguirre-Aguirre, Daniel; Izazaga-Pérez, Rafael; Granados-Agustín, Fermín; Villalobos-Mendoza, Brenda; Percino-Zacarías, M. E.; Cornejo-Rodriguez, Alejandro

doi:10.1117/1.oe.52.5.053606

Cited by 10 publications

(8 citation statements)

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“…It should be noted that as explained by wave theory, the fringes in the Ronchigram are caused by interference between the overlapping of diffracted wavefronts. 8,13,19 Therefore, this test can be seen as a lateral displacement interferometer that measures the slopes of the wavefront.…”

Section: Resultsmentioning

confidence: 99%

“…For this analysis, we used the software ReRRCA, described by Aguirre-Aguirre et al 19,38 This algorithm was proposed to obtain the wavefront aberrations of ronchigrams, using only one Ronchigram without the need for polynomial fits or trapezoidal integrations. For recovery of the coefficients ν, PSNR, and σ, we followed the process shown in the flowchart of Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The result is a skeleton of the interferogram formed by lines one pixel wide. 19 Currently, there are techniques that use all the information captured on the image, so the information is extracted from each pixel. One of these techniques is the phase shifting interferometry, 10,20 where three or more interference patterns are needed to obtain the wavefront phase.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…Recently, a similar procedure has been used to evaluate Ronchigrams. 11 What follows in this paper is the description of a procedure to estimate parameters and error function of any kind of mirror. By using the experimental setup shown in Fig.…”

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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