Abstract
For photon-counting based compressive imaging systems, it is difficult to obtain 3D image with intensity and depth information precisely due to the dead time and shot noise effect of photon-counting detectors. In this study, we design and achieve a 3D compressive imaging system using a single photon-counting detector. To overcome the radiometric distortion arising from the dead time and shot noise, considering the response mechanism of photon-counting detectors, a Bayesian posterior model is derived and a Reversible jump Markov chain Monte Carlo (RJMCMC)-based method is proposed to iteratively obtain model parameters. Experimental and simulation results indicate that the 3D image of targets can be effectively and accurately reconstructed with a smaller number of repeated illuminations and no longer restricted by the photon flux conditions (i.e., breaking through the upper limit of the received signal level). The proposed Bayesian RJMCMC-based radiometric correction method is not only beneficial to single-photon 3D compressive imaging system, but also to any other photon-counting based systems, e.g., photon-counting lidars. In addition, limiting condition of recovering the actual photon number for photon-counting imaging or lidar systems is also quantitatively analyzed, which is of great significance to the system scheme design.
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