Terahertz compressive imaging: understanding and improvement by a better strategy for data selection | |
Xing CG(邢春贵)4; Qi F(祁峰)1,2,3; Liu ZY(刘朝阳)1,2,3; Wang YL(汪业龙)1,2,3; Guo SX(郭树旭)4 | |
刊名 | International Journal of Numerical Modelling: Electronic Networks, Devices and Fields |
2021 | |
卷号 | 34期号:5页码:1-10 |
关键词 | compressive sensing experimental assessment image quality enhancement terahertz communication terahertz imaging |
ISSN号 | 0894-3370 |
产权排序 | 2 |
英文摘要 | 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. |
资助项目 | Independent 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] |
WOS研究方向 | Engineering ; Mathematics |
语种 | 英语 |
WOS记录号 | WOS:000610788000001 |
资助机构 | Independent 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] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/28302] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Qi F(祁峰); Guo SX(郭树旭) |
作者单位 | 1.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China 2.Key Lab of Image Understanding and Computer Vision, Shenyang, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 4.State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China |
推荐引用方式 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,2021,34(5):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,34(5),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 34.5(2021):1-10. |
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