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Related Experiment Video

Updated: Sep 20, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Retina-like Computational Ghost Imaging for an Axially Moving Target.

Yingqiang Zhang¹, Jie Cao^1,2, Huan Cui¹

¹Key Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

Computational ghost imaging (CGI) can now reconstruct moving targets using a novel retina-like pattern. This method improves image quality for dynamic scenes, overcoming limitations of traditional optical imaging.

Keywords:

computational ghost imaging image reconstruction technique retina-like imaging target axial motion

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Last Updated: Sep 20, 2025

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Area of Science:

Optics and Imaging
Computational Imaging
Signal Processing

Background:

Traditional optical imaging struggles with dynamic scenes.
Computational ghost imaging (CGI) reconstructs images using illumination patterns and single-pixel detection.
Target motion degrades CGI reconstruction quality in dynamic scenes.

Purpose of the Study:

To develop a time-variant retina-like computational ghost imaging method.
To address the challenge of reconstructing axially moving targets in dynamic scenes.
To enhance the quality of reconstructed images for moving targets.

Main Methods:

Designed retina-like illumination patterns with adjustable foveal radii.
Employed time-variant patterns synchronized with target axial movement.
Utilized compressive sensing algorithms for image reconstruction.

Main Results:

Achieved high-quality imaging results for axially moving targets.
Demonstrated significant improvement in reconstruction quality compared to standard CGI.
Experimental verification confirmed the method's effectiveness.

Conclusions:

The proposed retina-like CGI method effectively reconstructs moving targets.
This approach overcomes motion-induced degradation in dynamic scenes.
It offers a valuable reference for high-quality ghost imaging of moving objects.