Deep neural network for fringe pattern filtering and normalisation

Reyes-Figueroa, Alan; Rivera, Mariano

doi:10.48550/arxiv.1906.06224

Cited by 2 publications

(5 citation statements)

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“…In order to normalize the variations of intensity of the background and the contrast, as well as filtering-out the noise, we apply a pre-filtering process such as the ones proposed in [14,26,27,29,30,[36][37][38][39] and we obtain the normalized interferograms:…”

Section: Two-step Phase Shifting Methodologymentioning

confidence: 99%

“…We present the implementation of the pre-filtering process by using the Deep Neural Network (DNN) proposed by Rivera and Reyes [36]. An example of these normalized patterns is shown in Figure 5.…”

Section: Normalization By Deep Neural Network (Dnns)mentioning

confidence: 99%

“…In Figures 16c and 16g we show the normalized patterns using Deep Neural Networks as proposed in [36][37][38][39]. In this case, we present the implementation of the V-Net proposed in [36].…”

Section: Optical Experimental Datamentioning

confidence: 99%

“…Finally, the DNNs have grown in applications such as de-noising and normalization of interferograms as presented in Refs. [36][37][38][39]. Therefore we will evaluate all the algorithms by using these three normalization method for preprocessing the FPs.…”

Section: Introductionmentioning

confidence: 99%

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Two-step phase shifting algorithms: Where are we?

Flores

Reyes-Figueroa

Carrillo-Delgado³

et al. 2020

Optics & Laser Technology

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Two steps phase shifting interferometry has been a hot topic in the recent years. We present a comparison study of 12 representative self-tunning algorithms based on two-steps phase shifting interferometry. We evaluate the performance of such algorithms by estimating the phase step of synthetic and experimental fringe patterns using 3 different normalizing processes: Gabor Filters Bank (GFB), Deep Neural Networks (DNNs) and Hilbert Huang Transform (HHT); in order to retrieve the background, the amplitude modulation and noise. We present the variants of state-of-the-art phase step estimation algorithms by using the GFB and DNNs as normalization preprocesses, as well as the use of a robust estimator such as the median to estimate the phase step. We present experimental results comparing the combinations of the normalization processes and the two steps phase shifting algorithms. Our study demonstrates that the quality of the retrieved phase from of two-step interferograms is more dependent of the normalizing process than the phase step estimation method.

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Section: Two-step Phase Shifting Methodologymentioning

confidence: 99%

Section: Normalization By Deep Neural Network (Dnns)mentioning

confidence: 99%

Section: Optical Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two-step phase shifting algorithms: Where are we?

Flores

Reyes-Figueroa

Carrillo-Delgado³

et al. 2020

Optics & Laser Technology

Self Cite

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show abstract

“…In the specific context of interferometry, very few attempts exist to date. They have been used to identify and count fringes in [16][17][18]. In [19] and [20] they have been used to pre-process self mixing traces and in [21] they are used as a part of a self-mixing blood pressure measurement scheme.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for self-mixing interferometric displacement sensing

Barland,

Gustave

2021

Preprint

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Self mixing interferometry is a well established interferometric measurement technique. In spite of the robustness and simplicity of the concept, interpreting the self-mixing signal is often complicated in practice, which is detrimental to measurement availability. Here we discuss the use of a convolutional neural network to reconstruct the displacement of a target from the self mixing signal in a semiconductor laser. The network, once trained on periodic displacement patterns, can reconstruct arbitrarily complex displacement in different alignment conditions and setups. The approach validated here is amenable to generalization to modulated schemes or even to totally different self mixing sensing tasks.

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Deep neural network for fringe pattern filtering and normalisation

Cited by 2 publications

References 0 publications

Two-step phase shifting algorithms: Where are we?

Two-step phase shifting algorithms: Where are we?

Convolutional neural network for self-mixing interferometric displacement sensing

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