Fast high quality computational ghost imaging based on saliency variable sampling detection.

Xuan Liu, Jun Hu, Mingchi Ju, Yingzhi Wang, Tailin Han, Jipeng Huang, Cheng Zhou, Yongli Zhang, Lijun Song

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Xuan LiuCollege of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.

Jun HuCollege of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.

Mingchi JuCollege of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.

Yingzhi WangCollege of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.

Tailin HanCollege of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China. hantl@cust.edu.cn.

Jipeng HuangCollege of physics, Northeast Normal University, Changchun, 130024, China.

Cheng ZhouCollege of physics, Northeast Normal University, Changchun, 130024, China. zhoucheng91210@163.com.

Yongli ZhangAcademy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing, 100125, China.

Lijun SongChangchun Institute of Technology, Changchun, 130103, China. ccdxslj@126.com.

Funding

Key Research and Development Projects of Jilin Province Science and Technology Department 20210201042GXScience and Technology Planning Project of Jilin Province 20200404141YYScience Foundation of the Education Department of Jilin Province JJKH20221150KJSpecial Funds for Provincial Industrial Innovation in Jilin Province 2019C025

6 · The paper itself

Abstract

Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate

Identifiers

PMID38565578

DOI10.1038/s41598-024-57866-6

PMCPMC10987625

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.