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High-accuracy off-axis wavefront reconstruction from noisy data: local least square with multiple adaptive windows.

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    A new variational algorithm enhances object wavefront reconstruction from noisy holography data. This method achieves superior accuracy in phase and amplitude, improving optical imaging quality.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Computational Physics

    Background:

    • Off-axis holography is crucial for wavefront reconstruction.
    • Noisy intensity observations present challenges in accurate reconstruction.
    • Existing methods may lack robustness in complex scenarios.

    Purpose of the Study:

    • To develop a variational algorithm for robust object wavefront reconstruction.
    • To improve accuracy and quality in phase and amplitude retrieval.
    • To address challenges posed by noisy intensity data in off-axis holography.

    Main Methods:

    • A variational algorithm based on local least squares.
    • Utilizing multiple reconstructions with varying window sizes and directions.
    • Employing a statistical rule for optimal window selection per pixel.
    • Aggregating directional estimates for final wavefront reconstruction.

    Main Results:

    • The algorithm demonstrates high-quality wavefront reconstruction.
    • Exceptional accuracy achieved for both phase and amplitude components.
    • Validated through simulations and real-world data processing.
    • Robust performance in the presence of noise.

    Conclusions:

    • The developed variational algorithm offers a significant advancement in wavefront reconstruction.
    • It provides a reliable and accurate method for off-axis holography.
    • The technique shows potential for various optical imaging applications.