Compressed polarimetric ghost imaging of different material's reflective objects

Abstract

In recent years, ghost imaging has been received more and more attention. Here, a computational polarimetric ghost imaging of the different material reflective objects which uses compressive sensing algorithm is reported. In contrast with conventional ghost imaging, the scheme obtains the depolarization parameter of the reflected light from the objects, and both the polarization and reflected information of the objects can be retrieved. The results show that this imaging technique provides higher effective recognition ability to distinguish the different material objects than that of conventional ghost imaging when they have different polarization parameters. A simple multi-image information fusion method is proposed to improve the efficiency of object recognition.