Optimization of retina-like illumination patterns in ghost imaging

TL;DR

This paper introduces an optimized retina-like pattern design for ghost imaging that enhances region of interest quality without increasing measurements, verified through simulations and experiments.

Contribution

It proposes a novel method to optimize retina-like patterns using object sparsity priors, improving ROI imaging quality in ghost imaging.

Findings

Optimized retina-like patterns outperform conventional methods in ROI quality.

The method demonstrates good generalization ability across different scenarios.

Simulation and experimental results validate the effectiveness of the approach.

Abstract

Ghost imaging (GI) reconstructs images using a single-pixel or bucket detector, which has the advantages of scattering robustness, wide spectrum and beyond-visual-field imaging. However, this technique needs large amount of measurements to obtain a sharp image. There have been a lot of methods proposed to overcome this disadvantage. Retina-like patterns, as one of the compressive sensing approaches, enhance the imaging quality of region of interest (ROI) while not increase measurements. The design of the retina-like patterns determines the performance of the ROI in the reconstructed image. Unlike the conventional method to fill in ROI with random patterns, we propose to optimize retina-like patterns by filling in the ROI with the patterns containing the sparsity prior of objects. This proposed method is verified by simulations and experiments compared with conventional GI, retina-like GI and GI using patterns optimized by principal component analysis. The method using optimized retina-like patterns obtain the best imaging quality in ROI than other methods. Meanwhile, the good generalization ability of the optimized retina-like pattern is also verified. While designing the size and position of the ROI of retina-like pattern, the feature information of the target can be obtained to optimize the pattern of ROI. This proposed method paves the way for realizing high-quality GI.