Adaptive polarization compressive ghost imaging based on principal component analysis

Abstract

Ghost imaging is an imaging mechanism that can non-locally obtain an unknown object’s information with a single-pixel detector by the correlation of intensity fluctuations. To overcome the drawback of polarization compressive ghost imaging (PCGI), here we develop a novel adaptive polarization compressive ghost imaging (APCGI) method. By performing principal component analysis of the polarization statistics, we can compute the optimum unequal weighting coefficients forming as linear combinations of the light into bucket detectors. The specific steps of APCGI include calculating complete polarization parameters on the background, performing principal components analysis for optimal parameters, obtaining the information on the target-with-background scene. Experimental results demonstrate that adaptive polarization compressive ghost imaging performs better in restraining the background and pop out the details of targets as well as obtains better image quality.