# Edge detection based on joint iteration ghost imaging

## TL;DR

This paper introduces a novel ghost imaging-based edge detection method using joint iteration of Landweber regularization and guided filtering, enabling high-quality imaging and edge detection with fewer measurements.

## Contribution

It proposes a new joint iteration approach that enhances feature detection and image quality in ghost imaging, reducing measurement time and improving practical applicability.

## Key findings

- Successful recovery of spatial and edge information from speckle patterns
- Significant improvement in edge image quality
- Effective imaging with low measurement times

## Abstract

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system require a large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of projected Landweber iteration regularization and guided filter ghost imaging method have been proposed which can be improved the feature detection quality in ghost imaging. This method can also achieve high quality imaging. Simulation and experiment results show that the spatial information and edge information of target object are successfully recovered from the random speckle patterns without special coding under a low measurement times, and the edge image quality is improved remarkably. This approach improves the the applicability of ghost imaging, and can satisfy the practical application fields of imaging and edge detection at the same time.