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Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting

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    This study introduces a deep learning method for dual-wavelength interferometry, requiring only three interferograms to accurately retrieve phase information. This approach simplifies phase retrieval in multiwavelength applications.

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    Area of Science:

    • Optics and Photonics
    • Biomedical Imaging
    • Computational Science

    Background:

    • Dual-wavelength interferometry faces challenges in efficiently retrieving phase information from minimal interferograms.
    • Current methods often require a larger number of wavelength-multiplexed interferograms for accurate phase extraction.

    Purpose of the Study:

    • To propose a novel deep learning-based method for dual-wavelength interferogram decoupling.
    • To enable efficient phase retrieval using a reduced number of interferograms.

    Main Methods:

    • A deep neural network was trained to process three randomly phase-shifted dual-wavelength interferograms.
    • The method generates interferograms with arbitrary phase shifts for each wavelength.
    • Iterative phase retrieval algorithms extract wrapped phases, followed by synthetic beat wavelength phase calculation.

    Main Results:

    • The deep learning model successfully retrieves phase information from a minimal set of interferograms.
    • Simulations demonstrated the method's feasibility for complex structures like spherical caps and red blood cells.

    Conclusions:

    • The proposed deep learning method offers an efficient solution for phase retrieval in dual-wavelength interferometry.
    • This technique enhances phase retrieval capabilities for multiwavelength interferometric systems.