# Optimization of light fields in ghost imaging using dictionary learning

## TL;DR

This paper introduces a novel method to optimize light fields in ghost imaging using dictionary learning, significantly enhancing image quality especially at low sampling rates by employing matrix optimization and sparsity priors.

## Contribution

It proposes a new light field optimization approach in ghost imaging through dictionary learning and matrix optimization, improving reconstruction quality over existing methods.

## Key findings

- Enhanced imaging quality at low sampling rates
- Closed-form solution for sampling matrix derived
- Superior performance demonstrated via simulations and experiments

## Abstract

Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior of objects. A closed-form solution of the sampling matrix, which enables successive sampling, is derived. Through simulation and experimental results, it is shown that the proposed scheme leads to better imaging quality compared to the state-of-the-art optimization methods for light fields, especially at a low sampling rate.