Source coding model for repeated snapshot imaging

TL;DR

This paper models repeated snapshot imaging as a source coding problem, deriving a quantitative relation between measurement number and error rate, verified experimentally, advancing the application of information theory to ghost imaging.

Contribution

It introduces a source coding framework for repeated snapshot imaging and derives a formula linking measurement number and error rate, supported by experimental validation.

Findings

Derived a formula relating measurement number to error rate.

Experimental verification using pseudo-thermal light confirms the model.

Bridges information theory with ghost imaging studies.

Abstract

Imaging based on successive repeated snapshot measurement is modeled as a source coding process in information theory. The necessary number of measurement to maintain a certain level of error rate is depicted as the rate-distortion function of the source coding. Quantitative formula of the error rate versus measurement number relation is derived, based on the information capacity of imaging system. Second order fluctuation correlation imaging (SFCI) experiment with pseudo-thermal light verifies this formula, which paves the way for introducing information theory into the study of ghost imaging (GI), both conventional and computational.