Multi-imaging and Bayesian estimation for photon counting with EMCCD's

TL;DR

This paper introduces a Bayesian multi-imaging approach to enhance photon counting accuracy with EMCCDs, effectively reducing noise and improving detection in low-light conditions.

Contribution

It proposes a novel multi-imaging Bayesian estimation method that accounts for EMCCD gain variability and multiple photon events, improving photon counting accuracy.

Findings

Two-thirds of EMCCD-induced variance is removed in low-light areas.

Method improves photon detection accuracy even with high dynamic range images.

Physical photon noise becomes the dominant noise source after correction.

Abstract

A multi-imaging strategy is proposed and experimentally tested to improve the accuracy of photon counting with an electron multiplying charge-coupled device (EMCCD), by taking into account the random nature of its on-chip gain and the possibility of multiple photo-detection events on one pixel. This strategy is based on Bayesian estimation on each image, with a priori information given by the sum of the images. The method works even for images with large dynamic range, with more improvement in the low light level areas. In these areas, two thirds of the variance added by the EMCCD in a conventional imaging mode are removed, making the physical photon noise predominant in the detected image.