Imaging reconstruction comparison of different ghost imaging algorithms

Hong-Chao Liu

doi:10.1038/s41598-020-71642-2

Abstract

As an indirect and computational imaging approach, imaging reconstruction efficiency is critical for ghost imaging (GI). Here, we compare different GI algorithms, including logarithmic GI and exponential GI we proposed, by numerically analysing their imaging reconstruction efficiency and error tolerance. Simulation results show that compressive GI algorithm has the highest reconstruction efficiency due to its global optimization property. Error tolerance studies further manifest that compressive GI and exponential GI are sensitive to the error ratio. By replacing the bucket input of compressive GI with different bucket object signal functions, we integrate compressive GI with other GI algorithms and discuss their imaging efficiency. With the combination between the differential GI (or normalized GI) and compressive GI, both reconstruction efficiency and error tolerance will present the best performance. Moreover, an optical encryption is proposed by combining logarithmic GI, exponential GI and compressive GI, which can enhance the encryption security based on GI principle.

Highlights

compressive ghost imaging (CGI) can recover an image with the quality comparable to the ones of differential ghost imaging (DGI) and normalized ghost imaging (NGI) by performing one order less measurements
Simulations based on the computational ghost imaging (GI) scheme have manifested that compressive ghost imaging (CGI) algorithm has the highest reconstruction efficiency due to its global optimization property
Error tolerance studies have further demonstrated that CGI and efficiency of CGI (EGI) are sensitive to the error ratio

Summary

Introduction

CGI can recover an image with the quality comparable to the ones of DGI and NGI by performing one order less measurements. Because CGI has its unique merit in the imaging reconstruction calculation as demonstrated above, we further apply different F(Ioi) in other algorithms to replace the bucket object signal Ioi of the CGI as the input, and discuss their imaging efficiency.

Results

Conclusion