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    Area of Science:

    • Optics
    • Computational Imaging
    • Holography

    Background:

    • Optical scanning holography (OSH) is a single-pixel imaging (SPI) technique that requires complex optical setups.
    • Computational OSH (COSH) simplifies OSH using a spatial light modulator (SLM) but is limited by SLM refresh rates.
    • Conventional OSH with compressive sensing faces artifacts due to spiral scanning trajectories.

    Purpose of the Study:

    • To propose a random sparse sampling method for COSH to overcome trajectory limitations and reduce artifacts.
    • To apply compressive sensing for reconstructing object images from randomly sampled holograms.
    • To validate the proposed method numerically and experimentally.

    Main Methods:

    • Implementing COSH with discretely displayed Fresnel zone patterns (FZPs) on an SLM.
    • Utilizing random sparse sampling of holograms instead of sequential scanning.
    • Applying compressive sensing algorithms for image reconstruction.

    Main Results:

    • The proposed method successfully reconstructs object images from randomly subsampled holograms.
    • Experimental results demonstrate object identification with a sampling rate as low as 5%.
    • Random sparse sampling effectively reduces artifacts compared to spiral trajectories.

    Conclusions:

    • Random sparse sampling in COSH overcomes SLM refresh rate and trajectory limitations.
    • This approach enhances the efficiency and robustness of single-pixel holography.
    • The method shows significant potential for practical applications in computational imaging.