A numerical investigation on the convergence issues for ghost imaging

TL;DR

This paper investigates the convergence issues in ghost imaging, identifying key factors affecting data acquisition speed and proposing simulation methods to enhance practical applications.

Contribution

It introduces a convergence criterion for ghost imaging and explores how object features and light coherence influence data requirements, aiding real-time application development.

Findings

Object feature size relative to coherence length affects data needed

Convergence speed depends on specific experimental parameters

Simulation methods for thermal light ensembles are developed

Abstract

The long time consumption is a bottleneck for the applicability of the ghost imaging (GI). By introducing a criterion for the convergence of GI, we investigate a factor that impacts on the convergence speed of it. Based on computer experiments, we demonstrate that the object's feature size relative to the spatial coherent length of the illuminating light impacts on the necessary number of uncorrelated data being acquired for the correlation computation. It may motivate people to seek ways towards real-time practical applications of GI and its analogues. In addition, the method to simulate the uncorrelated sequence of a complex ensemble for thermal light is valuable in applications where actively controlling the light fields is needed.