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Target extraction through strong scattering disturbance using characteristic-enhanced pseudo-thermal ghost imaging

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【作者】 邹璇彭凡黄贤伟谭威周立宇朱孝辉付芹梁小茜南苏琴白艳锋傅喜泉

【Author】 Xuanpengfan Zou;Xianwei Huang;Wei Tan;Liyu Zhou;Xiaohui Zhu;Qin Fu;Xiaoqian Liang;Suqin Nan;Yanfeng Bai;Xiquan Fu;College of Computer Science and Electronic Engineering, Hunan University;Hunan Police Academy;Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy;School of Computer Science, Hunan University of Technology and Business;

【通讯作者】 白艳锋;傅喜泉;

【机构】 College of Computer Science and Electronic Engineering, Hunan UniversityHunan Police AcademyHunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademySchool of Computer Science, Hunan University of Technology and Business

【摘要】 It is difficult to extract targets under strong environmental disturbance in practice. Ghost imaging (GI) is an innovative antiinterference imaging technology. In this paper, we propose a scheme for target extraction based on characteristicenhanced pseudo-thermal GI. Unlike traditional GI which relies on training the detected signals or imaging results, our scheme trains the illuminating light fields using a deep learning network to enhance the target’s characteristic response.The simulation and experimental results prove that our imaging scheme is sufficient to perform single-and multiple-target extraction at low measurements. In addition, the effect of a strong scattering environment is discussed, and the results show that the scattering disturbance hardly affects the target extraction effect. The proposed scheme presents the potential application in target extraction through scattering media.

【Abstract】 It is difficult to extract targets under strong environmental disturbance in practice. Ghost imaging(GI) is an innovative antiinterference imaging technology. In this paper, we propose a scheme for target extraction based on characteristicenhanced pseudo-thermal GI. Unlike traditional GI which relies on training the detected signals or imaging results, our scheme trains the illuminating light fields using a deep learning network to enhance the target’s characteristic response.The simulation and experimental results prove that our imaging scheme is sufficient to perform single-and multiple-target extraction at low measurements. In addition, the effect of a strong scattering environment is discussed, and the results show that the scattering disturbance hardly affects the target extraction effect. The proposed scheme presents the potential application in target extraction through scattering media.

【基金】 supported by the National Natural Science Foundation of China (Nos. 61971184, 62001162, and62101187);the Hunan Provincial Natural Science Foundation(No. 2022JJ40091);the Fundamental Research Funds for the Central Universities (No. 531118010757)
  • 【文献出处】 Chinese Optics Letters ,中国光学快报(英文版) , 编辑部邮箱 ,2024年12期
  • 【分类号】TP391.41
  • 【下载频次】4
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