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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Materials Science

    Background:

    • Digital cameras rely on regular detector arrays for optical sampling.
    • Regular sampling, while optimal for 2D, can cause aliasing at high frequencies beyond the Nyquist limit.

    Purpose of the Study:

    • To introduce a randomization procedure for generating 2D hyperuniform patterns.
    • To suppress aliasing in image retrieval using these novel patterns.

    Main Methods:

    • A single-pixel camera was employed for experiments, allowing non-Cartesian sampling patterns.
    • Hyperuniform patterns were generated through a randomization procedure.
    • Image reconstruction quality was evaluated using root mean squared error and moiré fringe analysis.

    Main Results:

    • Images reconstructed with hyperuniform patterns showed lower root mean squared error.
    • Hyperuniform patterns effectively reduced moiré fringes at high frequencies compared to regular square patterns.
    • The findings suggest applicability to conventional detector arrays by utilizing manufacturing imperfections.

    Conclusions:

    • Hyperuniform patterns offer a superior method for optical sampling, mitigating aliasing issues.
    • Randomized sampling strategies can enhance image retrieval accuracy and quality.
    • Manufacturing imperfections in detector arrays can be leveraged for improved anti-aliasing performance.