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Adaptive weighted Gerchberg-Saxton algorithm for generation of phase-only hologram with artifacts suppression.

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    An adaptive weighted Gerchberg-Saxton (GS) algorithm improves image reconstruction by ensuring convergence and reducing artifacts. This novel feedback method enhances peak signal-to-noise ratio for high-quality optical reconstruction.

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    Area of Science:

    • Computational imaging
    • Optical reconstruction algorithms
    • Digital holography

    Background:

    • Conventional Gerchberg-Saxton (GS) algorithms utilize feedback to accelerate convergence.
    • However, this feedback can lead to iteration divergence, compromising reconstruction quality.
    • Artifacts in optical reconstruction further degrade the final image.

    Purpose of the Study:

    • To propose an adaptive weighted GS algorithm that ensures convergence and suppresses artifacts.
    • To enhance the peak signal-to-noise ratio (PSNR) in image reconstruction.
    • To achieve high-quality optical reconstruction for augmented reality devices.

    Main Methods:

    • Developed a novel adaptive feedback mechanism to replace conventional feedback in the GS algorithm.
    • Introduced an approximate quadratic phase to mitigate reconstruction artifacts.
    • Validated the proposed method through numerical simulations and optical experiments.

    Main Results:

    • The adaptive weighted GS algorithm demonstrated ensured convergence, unlike the conventional method.
    • Achieved an average improvement of 4.8 dB in peak signal-to-noise ratio (PSNR).
    • Successfully reconstructed high-quality images free from artifacts in an augmented reality device.

    Conclusions:

    • The proposed adaptive weighted GS algorithm effectively overcomes the divergence issue of conventional methods.
    • The integration of approximate quadratic phase significantly suppresses artifacts in optical reconstruction.
    • The validated method enables high-fidelity image reconstruction for advanced optical systems.