Braasch M, Gili VF, Pertsch T, Setzpfandt F (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 13
Article Number: 9450015
Journal Issue: 3
DOI: 10.1109/JPHOT.2021.3087753
Ghost Imaging has been extensively explored for 25 years for a two reasons: the rich physics of second-order photon correlations that enable this imaging scheme and the possibility of implementing new imaging protocols with interesting real-life applications, e.g. imaging in turbulent media, investigation of sensitive samples in low-flux regimes, 3-D plenoptic imaging, and so on. Since the first demonstration of Ghost Imaging, several extended versions of the Traditional Ghost Imaging algorithm have been proposed, such as Correspondence Ghost Imaging, Pseudo-Inverse Ghost Imaging, and normalization techniques that rely on different computational approaches to obtain the image from measured data. So far, a direct comparison of all above-mentioned protocols for the same experimental parameters is still lacking. In this work, we experimentally and numerically implement a number of different methods and systematically compare them in terms of the obtained SNR and computational cost. Furthermore, we investigate their compatibility with Correlation Plenoptic Imaging, a technique strictly connected to Ghost Imaging, that allows refocusing of images, increasing the depth of field (DOF) and making 3D visualization possible. Our results can provide useful guidelines for the choice of a suitable numerical algorithm for in the light of Ghost Imaging applications.
APA:
Braasch, M., Gili, V.F., Pertsch, T., & Setzpfandt, F. (2021). Classical Ghost Imaging: A Comparative Study of Algorithmic Performances for Image Reconstruction in Prospect of Plenoptic Imaging. IEEE Photonics Journal, 13(3). https://doi.org/10.1109/JPHOT.2021.3087753
MLA:
Braasch, Marie, et al. "Classical Ghost Imaging: A Comparative Study of Algorithmic Performances for Image Reconstruction in Prospect of Plenoptic Imaging." IEEE Photonics Journal 13.3 (2021).
BibTeX: Download