Reconstructing compound objects by quantum imaging with higher-order correlation functions

TL;DR

This paper introduces an efficient iterative quantum imaging method that reconstructs complex objects using higher-order correlation functions, significantly reducing computational complexity and achieving super-resolution with experimental validation.

Contribution

The authors propose a novel iterative inference scheme based on Fisher information that simplifies quantum image reconstruction, enabling super-resolution with fewer parameters.

Findings

The method achieves super-resolution in quantum imaging.

Experimental data confirms the effectiveness of higher-order correlations.

Optimal photon correlations enhance image resolution.

Abstract

Quantum imaging has a potential of enhancing precision of the object reconstruction by using quantum correlations of the imaging field. This is especially important for imaging requiring low-intensity fields up to the level of few-photons. However, quantum imaging generally leads to nonlinear estimation problems. The complexity of these problems rapidly increases with the number of parameters describing the object. We suggest a way to drastically reduce the complexity for a wide class of problems. The key point of our approach is connecting the features of the Fisher information with the parametric locality of the problem, and building the efficient iterative inference scheme reconstructing only a subset of the whole set of parameters in each step. This iterative scheme is linear on the total number of parameters. This scheme is applied to quantum near-field imaging, the inference procedure is developed resulting in super-resolving reconstruction of grey compound transmission objects. The functionality of the method is demonstrated with experimental data obtained by measurements of higher-order correlation functions for imaging with entangled twin-photons and pseudo-thermal light sources. By analyzing the informational content of the measurement, it becomes possible to predict the existence of optimal photon correlations providing for the best image resolution in the super-resolution regime. This prediction is experimentally confirmed. It is also shown how an estimation bias stemming from image features may drastically improve the resolution.