Photon-counting statistics-based support vector machine with multi-mode photon illumination for quantum imaging

Abstract

We propose a photon-counting-statistics-based imaging process for quantum imaging where background photon noise can be distinguished and eliminated by photon mode estimation from the multi-mode Bose-Einstein distribution. Photon-counting statistics show multi-mode behavior in a practical, low-cost single-photon-level quantum imaging system with a short coherence time and a long measurement time interval. Different mode numbers in photon-counting probability distributions from single-photon illumination and background photon noise can be classified by a machine learning technique such as a support vector machine (SVM). The proposed photon-counting statistics-based support vector machine (PSSVM) learns the difference in the photon-counting distribution of each pixel to distinguish between photons from the source and the background photon noise to improve the image quality. We demonstrated quantum imaging of a binary-image object with photon illumination from a spontaneous parametric down-conversion (SPDC) source. The experiment results show that the PSSVM applied quantum image improves a peak signal-to-noise ratio (PSNR) gain of 2.89dB and a structural similarity index measure (SSIM) gain of 27.7% compared to the conventional direct single-photon imaging.