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    Area of Science:

    • Optics
    • Computer Science
    • Holography

    Background:

    • Learning-based computer-generated holograms (CGHs) show promise for real-time, high-quality holographic displays.
    • Existing algorithms often process complex-valued wave fields as two-channel images, not fully utilizing complex amplitude characteristics.

    Purpose of the Study:

    • To propose a novel dual-channel parallel neural network (DCPNet) for efficient phase-only hologram (POH) generation.
    • To address limitations in current methods by better considering complex amplitude computational characteristics.

    Main Methods:

    • Developed a dual-channel parallel neural network (DCPNet) inspired by double phase amplitude encoding.
    • Encoded complex-valued wave fields into two real-valued phase elements instead of a two-channel image.
    • Synthesized POHs by sampling two learned sub-POHs with a complementary 2D binary grating.

    Main Results:

    • DCPNet achieved high-fidelity 2k POH generation in 36 milliseconds (ms) in simulations.
    • Optical experiments demonstrated superior preservation of finer details in reconstructed images.
    • The method effectively suppressed speckle noise and improved image uniformity.

    Conclusions:

    • The proposed DCPNet offers an effective and efficient approach for generating phase-only holograms.
    • This method significantly enhances the quality of reconstructed holographic images.
    • DCPNet holds potential for advancing real-time, high-quality holographic display technologies.