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Updated: Sep 13, 2025

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CAHVAE: generating CGHs with complex amplitude hologram variational autoencodern.

Bingsen Qiu, Jing Chen, Leshan Wang

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    |July 30, 2025
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    Summary

    This study introduces a new deep learning model, the complex amplitude hologram variational autoencoder (CAHVAE), for generating holograms. CAHVAE effectively synthesizes complex amplitude holograms, improving image quality and reducing noise in holographic displays.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Deep learning models for computer-generated holograms (CGH) struggle with limited data.
    • Existing models often use real-valued convolutions, failing to capture complex optical wave properties.

    Purpose of the Study:

    • To develop a novel deep learning approach for synthesizing complex amplitude holograms.
    • To overcome limitations of real-valued kernels in holographic data processing.

    Main Methods:

    • Proposed the complex amplitude hologram variational autoencoder (CAHVAE).
    • Utilized complex-valued encoder and decoder to process complex optical field data directly.
    • Modeled the latent space using a complex multivariate Gaussian distribution for efficient sampling.

    Main Results:

    • CAHVAE successfully generated novel complex amplitude holograms.
    • Demonstrated high-quality reconstruction of color holograms with preserved fine details.
    • Significantly reduced speckle noise and enhanced reconstructed image quality.

    Conclusions:

    • CAHVAE is an effective method for synthesizing complex amplitude holograms.
    • The model shows promise for high-fidelity holographic display applications.
    • Directly processing complex optical fields improves hologram generation quality.