Terahertz compressive imaging: understanding and improvement by a better strategy for data selection. (24th January 2021)

• Record Type: Journal Article
• Authors: Xing, Chungui; Qi, Feng; Liu, Zhaoyang; Wang, Yelong; Guo, Shuxu
• Other Names: Vadalà Valeria guestEditor.; Crupi Giovanni guestEditor.
• Is Part Of: International journal of numerical modelling. Volume 34:Number 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-24
• Subjects: compressive sensing -- experimental assessment -- image quality enhancement -- terahertz communication -- terahertz imaging; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2863
• Languages: English
• ISSNs: 0894-3370

Abstract

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.