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Experimental comparison of single-pixel imaging algorithms.

Liheng Bian, Jinli Suo, Qionghai Dai

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    Single-pixel imaging (SPI) reconstructs 2D images using a photodiode. This review compares SPI algorithms, finding trade-offs between efficiency, computation, and noise robustness for optimal reconstruction.

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    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Signal Processing

    Background:

    • Single-pixel imaging (SPI) offers advantages over traditional sensors, including high signal-to-noise ratio and wide spectral range.
    • Existing SPI reconstruction algorithms lack comprehensive comparative analysis, hindering further development and application.

    Purpose of the Study:

    • To review and compare existing single-pixel imaging reconstruction algorithms.
    • To introduce novel conjugate gradient descent (CGD) and Poisson maximum-likelihood (PML) based SPI algorithms.
    • To analyze trade-offs between capture efficiency, computational complexity, and noise robustness.

    Main Methods:

    • A unified framework for comparing linear correlation, alternating projection (AP), compressive sensing (CS) with total variation (TV) regularization, CGD, and PML algorithms.
    • Simulations and experimental validation of algorithm performance.
    • Evaluation of reconstruction accuracy, measurement count, running time, and noise robustness.

    Main Results:

    • CS-based TV regularization is efficient for small-scale SPI, requiring fewer measurements and less time.
    • CGD and AP methods exhibit faster performance in large-scale SPI reconstruction.
    • TV and AP methods demonstrate superior robustness against measurement noise.

    Conclusions:

    • No single SPI algorithm is universally optimal; trade-offs exist between capture efficiency, computational load, and noise resilience.
    • Algorithm selection depends on specific application requirements for single-pixel imaging.
    • Source code is available for non-commercial use to facilitate further research.