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    Area of Science:

    • Photon-counting imaging
    • 3D compressive sensing
    • Computational imaging

    Background:

    • Photon-counting detectors in 3D compressive imaging suffer from dead time and shot noise, limiting precise intensity and depth information acquisition.
    • Radiometric distortion is a significant challenge for accurate reconstruction in these systems.

    Purpose of the Study:

    • To design and implement a 3D compressive imaging system utilizing a single photon-counting detector.
    • To develop a method for overcoming radiometric distortion caused by detector dead time and shot noise.
    • To enable accurate 3D image reconstruction under reduced illumination conditions.

    Main Methods:

    • A Bayesian posterior model was derived based on the photon-counting detector response mechanism.
    • A Reversible jump Markov chain Monte Carlo (RJMCMC)-based method was employed for iterative model parameter estimation.
    • The system was validated through experimental and simulation results.

    Main Results:

    • The proposed method effectively and accurately reconstructs 3D images of targets.
    • Accurate reconstruction is achieved with a reduced number of repeated illuminations.
    • The system breaks through the upper limit of received signal levels, overcoming photon flux restrictions.

    Conclusions:

    • The Bayesian RJMCMC-based radiometric correction method significantly improves 3D compressive imaging with photon-counting detectors.
    • This approach is applicable to various photon-counting systems, including photon-counting lidars.
    • Quantitative analysis of recovering actual photon numbers provides crucial insights for system design.