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Automatized fringe pattern preprocessing using unsupervised variational image decomposition.

Maria Cywińska, Maciej Trusiak, Krzysztof Patorski

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    |September 13, 2019
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    Summary
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    A new unsupervised variational image decomposition (uVID) model effectively preprocesses fringe patterns for optical metrology. This method overcomes limitations of empirical mode decomposition (EMD), improving measurement accuracy by separating noise, background, and fringe information.

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    Area of Science:

    • Optical Metrology
    • Image Processing
    • Computational Imaging

    Background:

    • Fringe pattern preprocessing is critical for accurate full-field optical metrology, directly impacting measurement precision.
    • Existing methods like 2D Empirical Mode Decomposition (EMD) face challenges with mode-mixing, hindering effective separation of fringe pattern components (background, information, noise).
    • Adaptive and detail-preserving filtering is essential for robust fringe analysis.

    Purpose of the Study:

    • To introduce an unsupervised variational image decomposition (uVID) model specifically designed for fringe pattern preprocessing.
    • To address and overcome the mode-mixing limitations inherent in traditional data-driven decomposition methods like EMD.
    • To achieve automated, versatile, and robust sparse three-component fringe pattern decomposition.

    Main Methods:

    • Development of an unsupervised variational image decomposition (uVID) model tailored for fringe pattern analysis.
    • Customization of the VID calculation scheme, including a tolerance parameter for accurate fringe extraction and specific decomposition parameter values.
    • Integration of a fringe pattern-specific BM3D denoising algorithm with fixed parameter values.

    Main Results:

    • The proposed uVID model demonstrates successful sparse three-component decomposition of fringe patterns.
    • Numerical and experimental results show favorable comparisons of uVID against the reference 2D EMD algorithm and classical VID models.
    • The uVID method exhibits robustness, accommodating significant local variations in fringe pattern orientation, period, noise, contrast, and background.

    Conclusions:

    • The unsupervised variational image decomposition (uVID) model offers a significant advancement in fringe pattern preprocessing for optical metrology.
    • uVID effectively overcomes the mode-mixing issue, enabling more accurate and reliable separation of essential fringe pattern components.
    • The method's automation, versatility, and robustness make it a valuable tool for various fringe pattern-based metrology applications.