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Phase derivative estimation in digital holographic interferometry using a deep learning approach.

Allaparthi Venkata Satya Vithin, Ankur Vishnoi, Rajshekhar Gannavarpu

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    Summary
    This summary is machine-generated.

    This study introduces a deep learning method for precise phase derivative estimation in digital holographic interferometry. The Y-Net model accurately extracts phase derivatives, crucial for deformation metrology, even with noise.

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    Area of Science:

    • Optics and Photonics
    • Metrology
    • Artificial Intelligence

    Background:

    • Accurate phase derivative estimation is vital in digital holographic interferometry for analyzing object deformation.
    • Current methods face challenges in reliably extracting these derivatives from complex interference signals.

    Purpose of the Study:

    • To develop a deep learning-based approach for direct and simultaneous estimation of phase derivatives in digital holographic interferometry.
    • To assess the robustness and practical utility of the proposed method.

    Main Methods:

    • Implementation of a Y-Net deep learning model for direct phase derivative estimation.
    • Numerical simulations to evaluate performance under additive white Gaussian noise and speckle noise.
    • Experimental validation using digital holographic interferometry data for deformation metrology.

    Main Results:

    • The Y-Net model successfully estimates phase derivatives along both vertical and horizontal dimensions.
    • The approach demonstrates robustness against common noise types encountered in interferometry.
    • Practical utility confirmed through experimental deformation metrology.

    Conclusions:

    • Deep learning, specifically the Y-Net model, offers a powerful and robust solution for phase derivative estimation in digital holographic interferometry.
    • This method enhances the accuracy and reliability of deformation metrology applications.