Dual-mode adaptive-SVD ghost imaging

TL;DR

This paper introduces a dual-mode adaptive SVD ghost imaging technique that switches between high-quality imaging and edge detection, using adaptive thresholding and fewer measurements for efficient data acquisition.

Contribution

It presents a novel dual-mode A-SVD ghost imaging method with adaptive foreground localization and a single-round measurement scheme, enhancing efficiency and functionality.

Findings

Achieves high-quality images with fewer samples.

Enables direct edge detection without original images.

Demonstrates effectiveness through simulations and experiments.

Abstract

In this paper, we present a dual-mode adaptive singular value decomposition ghost imaging (A-SVD GI), which can be easily switched between the modes of imaging and edge detection. It can adaptively localize the foreground pixels via a threshold selection method. Then only the foreground region is illuminated by the singular value decomposition (SVD) - based patterns, consequently retrieving high-quality images with fewer sampling ratios. By changing the selecting range of foreground pixels, the A-SVD GI can be switched to the mode of edge detection to directly reveal the edge of objects, without needing the original image. We investigate the performance of these two modes through both numerical simulations and experiments. We also develop a single-round scheme to halve measurement numbers in experiments, instead of separately illuminating positive and negative patterns in traditional methods. The binarized SVD patterns, generated by the spatial dithering method, are modulated by a digital micromirror device (DMD) to speed up the data acquisition. This dual-mode A-SVD GI can be applied in various applications, such as remote sensing or target recognition, and could be further extended for multi-modality functional imaging/detection.