Computational ghost imaging with discrete stochastic sources

Abstract

Lots of digital devices were widely used in computational ghost imaging (CGI) to serve as optical sources and detectors. These digital devices greatly affect the statistical behaviors of the CGI system. In this paper, we attribute all the influence of these digital devices to the discrete probability density functions of the optical sources. We investigate CGI with three discrete optical sources, which emit intensities fulfilling Bernoulli, binomial, and Poisson distributions. We clarify the inner relation between the bucket signals and the reference signals by using their joint density probability functions. In correlation measurement, the visibility and signal-to-noise ratio of the ghost images greatly depend on the probability statistics of the sources. We find that CGI with discrete sources can surpass the classical visibility limit one-third in the thermal-light case. The simulation results confirm our theoretical predictions. Thus, the utility of the digital devices makes CGI manipulable and applicable in practice.