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    Area of Science:

    • Optics and photonics
    • Computational imaging
    • Signal processing

    Background:

    • Single-pixel imaging (SPI) is susceptible to experimental noise.
    • Existing methods for noise reduction in SPI can be complex or limited in scope.

    Purpose of the Study:

    • To develop a novel method for reducing experimental noise in single-pixel imaging.
    • To enhance the robustness and practical applicability of SPI.

    Main Methods:

    • Expressing sampling pattern subsets as linear combinations of regular simplex vertices.
    • Utilizing binarized sampling and complementary sampling extensions.
    • Employing Fourier domain regularized inversion for real-time reconstruction.

    Main Results:

    • The proposed method effectively reduces experimental noise in SPI.
    • The modified measurement matrix yields non-negative elements suitable for spatial light modulators.
    • Measurements demonstrate increased robustness to ambient illumination and experimental conditions.
    • The optimal simplex dimension is shown to depend on the noise level.

    Conclusions:

    • The regular simplex-based sampling pattern offers a significant improvement in SPI noise reduction.
    • The method is practical for real-time applications and adaptable to different sampling schemes.
    • This approach enhances the reliability of single-pixel imaging systems.