Compressive ghost imaging

TL;DR

This paper introduces an advanced compressed sensing algorithm for pseudothermal ghost imaging that significantly reduces measurement requirements and improves image reconstruction quality, demonstrated through experimental data.

Contribution

It presents a novel compressed sensing-based algorithm that enhances ghost imaging efficiency and quality, applicable to existing experimental data.

Findings

Reduces measurements needed for image reconstruction by an order of magnitude.

Improves image quality in pseudothermal ghost imaging.

Demonstrated effectiveness with experimental data.

Abstract

We describe an advanced image reconstruction algorithm for pseudothermal ghost imaging, reducing the number of measurements required for image recovery by an order of magnitude. The algorithm is based on compressed sensing, a technique that enables the reconstruction of an N-pixel image from much less than N measurements. We demonstrate the algorithm using experimental data from a pseudothermal ghost-imaging setup. The algorithm can be applied to data taken from past pseudothermal ghost-imaging experiments, improving the reconstruction's quality.