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    This study presents a universal holographic autofocusing method that adapts to diverse data without manual tuning. It achieves precise focusing by estimating scene geometry and iteratively refining depth maps for robust generalization.

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    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Computer Vision

    Background:

    • Autofocusing in holography lacks a universal method adaptable to varying experimental parameters like wavelength and pixel pitch.
    • Existing approaches, including mathematical and learning-based methods, often require manual hyperparameter tuning and struggle with diverse holographic data.

    Purpose of the Study:

    • To introduce a universal holographic autofocusing methodology that eliminates manual hyperparameter tuning.
    • To develop a robust and adaptable approach for autofocusing diverse input holograms, including synthetic and real-world data.

    Main Methods:

    • Autonomous extraction of optimal numerical reconstruction distances for holograms.
    • Volumetric rendering of holograms to estimate underlying scene geometry.
    • Iterative hologram regeneration process constrained by ground-truth depth values for accuracy.

    Main Results:

    • Demonstrated superior robustness and generalization capabilities on both synthetic computer-generated holograms and optically acquired phase-shifting holograms.
    • Achieved precise autofocusing by autonomously estimating scene geometry and refining depth maps.
    • Eliminated the need for manual hyperparameter tuning, offering adaptability to diverse experimental setups.

    Conclusions:

    • The proposed universal methodology represents a significant advancement toward reliable autofocusing in various holographic applications.
    • The approach offers a robust solution for holographic autofocusing, overcoming limitations of existing methods.
    • This work paves the way for more accessible and versatile holographic imaging systems.