Modeling the behavior of signal-to-noise ratio for repeated snapshot imaging

TL;DR

This paper develops a theoretical model for how the signal-to-noise ratio (SNR) evolves with the number of measurements in static object imaging, validated through experiments with pseudo-thermal light.

Contribution

It introduces a novel theoretical model linking SNR behavior to the information capacity of optical systems, validated by experimental data for both direct averaging and ghost imaging.

Findings

Model accurately fits experimental data across conditions

SNR increases with measurement number as predicted by theory

Validates the use of pseudo-thermal light in imaging applications

Abstract

For imaging of static object by the means of sequential repeated independent measurements, a theoretical modeling of the behavior of signal-to-noise ratio (SNR) with varying number of measurement is developed, based on the information capacity of optical imaging systems. Experimental veritification of imaging using pseudo-thermal light source is implemented, for both the direct average of multiple measurements, and the image reconstructed by second order fluctuation correlation (SFC) which is closely related to ghost imaging. Successful curve fitting of data measured under different conditions verifies the model.