Target recognition in ghost imaging from traditional to advance; a brief review

Ayesha Abbas, Jianbang Mu, Zhang Mengyue, Jie Cao*, Xiaonan Zhang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Ghost Imaging (GI) has emerged as groundbreaking technique in image processing, offering unique advantages such as non-locality, simplified optical setups, high detection efficiency and robust performance in scattering media. Over the years, GI has evolved from fundamental research to real time applications, driven by advancements in methodologies and computational techniques. However, traditional optical approaches for target recognition often encounter limitations, particularly in achieving high accuracy and reliability under complex conditions. To overcome these challenges, the integration of deep learning (DL) and machine learning (ML) into GI systems presents a promising solutions, enabling enhanced performances with reduced constraints. This review provides a comprehensive analysis of target recognition methodologies in GI, comparing traditional optical techniques with cutting-edge DL and ML- based approaches. Furthermore, we discuss the key challenges faced by these methods and propose future directions to advance this innovative and interdisciplinary field, paving the way for more precise and scalable solutions in practical applications.

Original languageEnglish
Article number112450
JournalOptics and Laser Technology
Volume184
DOIs
Publication statusPublished - Jun 2025

Keywords

  • Deep learning
  • Ghost Imaging
  • Target recognition
  • Traditional methods of target recognition

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Abbas, A., Mu, J., Mengyue, Z., Cao, J., & Zhang, X. (2025). Target recognition in ghost imaging from traditional to advance; a brief review. Optics and Laser Technology, 184, Article 112450. https://doi.org/10.1016/j.optlastec.2025.112450