# Compressed ghost edge imaging

## TL;DR

This paper introduces a compressed ghost edge imaging framework that reconstructs object edges using fewer measurements through structured speckle patterns and compressed sensing, enhancing image quality over existing methods.

## Contribution

The paper presents a novel compressed ghost edge imaging scheme combining structured speckle patterns with compressed sensing for improved edge detection.

## Key findings

- Higher image quality achieved
- Reduces number of measurements needed
- Effective in experimental and numerical simulations

## Abstract

In this paper, we propose an advanced framework of ghost edge imaging, named compressed ghost edge imaging (CGEI). In the scheme, a set of structured speckle patterns with pixel shifting are illuminated on an unknown object, and the output is collected by a bucket detector without any spatial resolution. By using compressed sensing algorithm, we obtain the horizontal and vertical edge information of the unknown object with the bucket detector detection results and the known structured speckle patterns. The edge is finally constructed by the two-dimentional edge information. The experimental and numerical simulations results show that the proposed scheme has a higher quality and reduces the number of measurements, in comparison with the existed edge detection schemes based on ghost imaging.