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Updated: Jul 31, 2025

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy
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Computer holography using deep neural network with Fourier basis.

Runze Zhu, Lizhi Chen, Hao Zhang

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    A novel deep neural network approach enhances hologram generation quality and model generalization. This physics-informed strategy uses Fourier basis functions for improved spatial signal representation and reconstruction accuracy.

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    Area of Science:

    • Computational physics
    • Optics
    • Machine learning

    Background:

    • Deep neural networks (DNNs) offer potential for rapid hologram generation.
    • High-quality hologram reconstruction and model generalization depend heavily on the training dataset.
    • Existing methods may face limitations in achieving both speed and fidelity.

    Purpose of the Study:

    • To propose a novel deep neural network for phase hologram generation.
    • To introduce a physics-informed training strategy using Fourier basis functions.
    • To enhance the generalization capability and reconstruction quality of hologram generation models.

    Main Methods:

    • Developed a deep neural network architecture for phase hologram generation.
    • Implemented a physics-informed training strategy utilizing orthonormal Fourier basis functions.
    • Regulated spatial frequency characteristics by recombining Fourier basis functions in the frequency domain.

    Main Results:

    • Demonstrated effective improvement in model generalization.
    • Achieved high-quality reconstructions through numerical simulations.
    • Validated the proposed method with optical experiments.

    Conclusions:

    • The proposed physics-informed deep neural network strategy significantly enhances hologram generation.
    • Fourier basis functions provide orthonormal representations crucial for improved spatial signal processing.
    • The method offers a robust approach for high-fidelity and generalizable hologram generation.