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Terahertz compressive imaging: understanding and improvement by a better strategy for data selection
Xing CG(邢春贵)1; Qi F(祁峰)2,3,4; Liu ZY(刘朝阳)2,3,4; Wang YL(汪业龙)2,3,4; Guo SX(郭树旭)1
Department光电信息技术研究室
Source PublicationInternational Journal of Numerical Modelling: Electronic Networks, Devices and Fields
ISSN0894-3370
2021
Pages1-10
Indexed BySCI ; EI
EI Accession number20210509834544
WOS IDWOS:000610788000001
Contribution Rank2
Funding OrganizationIndependent project of robotics and intelligent manufacturing innovation institute, Chinese Academy of Sciences [C2019001] ; National Key Research and Development Program of China [2016YFC0102900] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61505089, 61605235]
Keywordcompressive sensing experimental assessment image quality enhancement terahertz communication terahertz imaging
Abstract

Compressive sensing (CS) is a novel sampling modality, which indicates the signals can be sampled at a rate much below the Nyquist sampling rate. CS has increasing interest recently due to high demand of rapid, efficient, and in-expensive signal processing applications in the μmWave and mmWave frequencies, such as communication and imaging. There have been a lot of theoretical studies on this topic, but there is a lack of systematic experimental analysis of the implementation method itself. In this paper, we have investigated the influencing factors of terahertz compressive sensing based on experimental results, including illumination and the size of the pixel. Besides, to differentiate from current approaches, which generally make full use of the data, we propose to sort the data first and select a part of them based on amplitude, which might deliver a better image by prompting the mathematical calculations compulsively. We believe that such considerations given above would help to make a better system design and improve the performance of compressive imaging, and these results will also be helpful in the application of terahertz communication.

Language英语
WOS SubjectEngineering, Electrical & Electronic ; Mathematics, Interdisciplinary Applications
WOS Research AreaEngineering ; Mathematics
Funding ProjectIndependent project of robotics and intelligent manufacturing innovation institute, Chinese Academy of Sciences[C2019001] ; National Key Research and Development Program of China[2016YFC0102900] ; National Natural Science Foundation of China[61505089] ; National Natural Science Foundation of China[61605235]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28302
Collection光电信息技术研究室
Corresponding AuthorQi F(祁峰); Guo SX(郭树旭)
Affiliation1.State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
3.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China
4.Key Lab of Image Understanding and Computer Vision, Shenyang, China
Recommended Citation
GB/T 7714
Xing CG,Qi F,Liu ZY,et al. Terahertz compressive imaging: understanding and improvement by a better strategy for data selection[J]. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields,2021:1-10.
APA Xing CG,Qi F,Liu ZY,Wang YL,&Guo SX.(2021).Terahertz compressive imaging: understanding and improvement by a better strategy for data selection.International Journal of Numerical Modelling: Electronic Networks, Devices and Fields,1-10.
MLA Xing CG,et al."Terahertz compressive imaging: understanding and improvement by a better strategy for data selection".International Journal of Numerical Modelling: Electronic Networks, Devices and Fields (2021):1-10.
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