A single-pixel terahertz imaging system based on compressed sensing

2008 ◽  
Vol 93 (12) ◽  
pp. 121105 ◽  
Author(s):  
Wai Lam Chan ◽  
Kriti Charan ◽  
Dharmpal Takhar ◽  
Kevin F. Kelly ◽  
Richard G. Baraniuk ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Single Pixel

scholarly journals Detecting the Trajectory of Moving Object for Single-Pixel Imaging System

2018 ◽  
Vol 160 ◽  
pp. 07002
Author(s):  
Shao Hui ◽  
Wu Dongsheng ◽  
Chen Jie ◽  
Li Zhao ◽  
Xu Xiaoxue ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Compressed Sensing ◽  
Imaging System ◽  
Moving Object ◽  
Weighted Sum ◽  
Angle Change ◽  
Ideal Situation ◽  
Simulation Results ◽  
Orientation Parameters ◽  
Single Pixel

In order to get the trajectory of moving object using single-pixel imaging system, an algorithm is proposed. The same pseudorandom masks are employed to illuminate the different time scene. A time weighted sum of the background correction signals is employed to get the trajectory information using compressed sensing (CS) method. In ideal situation, we can obtain other parameters (e.g., speed, orientation) besides the trajectory. However, the reflective intensity of the object can be change due to the reflective angle change caused by the motion in some situations. This will mislead for achieving the speed, orientation parameters. In order to eliminate this effect, a division method is utilized. At last, the computer simulation results prove the effect validity of the proposed algorithm.

A Terahertz Compressive Imaging Method Based on Single Detector of Randomly Moving Template

2013 ◽  
Vol 756-759 ◽  
pp. 3785-3788
Author(s):  
Sai Qi Shang ◽  
Min Gang Wang ◽  
Wei Li ◽  
Yao Yang
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Matching Pursuit ◽  
Terahertz Imaging ◽  
Compressive Sampling ◽  
Imaging Method ◽  
Original Image ◽  
Reconstruction Algorithms ◽  
Compressive Imaging ◽  
Single Detector

Expensiveness and lack of N-pixels sensor affect the application of terahertz imaging. New compressed sensing theory recently achieved a major breakthrough in the field of signal codec, making it possible to recover the original image by using the measured values, which have much smaller number than the pixels in the image. In this paper, by comparing the measurement matrices based on different reconstruction algorithms, such as Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit and Minimum L_1 Norm algorithms, we proposed a terahertz imaging method based on single detector of randomly moving measurement matrices, designed the mobile random templates and an automatically template changing mechanism, constructed a single detector imaging system, and completed the single terahertz detector imaging experiments.

Single-pixel terahertz imaging via compressed sensing

2011 ◽  
Author(s):  
Ya-qin Zhao ◽  
Liang-liang Zhang ◽  
Guo-teng Duan ◽  
Xiao-hua Liu ◽  
Cun-lin Zhang
Keyword(s):  
Compressed Sensing ◽  
Terahertz Imaging ◽  
Single Pixel

Enhancing the image resolution in a single-pixel sub-THz imaging system based on compressed sensing

2018 ◽  
Vol 57 (04) ◽  
pp. 1 ◽  
Author(s):  
Umit Alkus ◽  
Esra Sengun Ermeydan ◽  
Asaf Behzat Sahin ◽  
Ilyas Cankaya ◽  
Hakan Altan
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Image Resolution ◽  
Thz Imaging ◽  
Single Pixel

Compressed sensing spectral imaging for plasma optical emission spectroscopy

2016 ◽  
Vol 31 (11) ◽  
pp. 2198-2206 ◽  
Author(s):  
John D. Usala ◽  
Adrian Maag ◽  
Thomas Nelis ◽  
Gerardo Gamez
Keyword(s):  
Compressed Sensing ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Imaging System ◽  
Spectral Imaging ◽  
Optical Emission ◽  
First Time ◽  
Single Pixel ◽  
Plasma Optical Emission Spectroscopy

A single-pixel compressed sensing spectral imaging system is designed and implemented on plasma optical emission for the first time.

Imaging system of single pixel camera based on compressed sensing

2012 ◽  
Vol 20 (11) ◽  
pp. 2523-2530 ◽  
Author(s):  
陈涛 CHEN Tao ◽  
李正炜 LI Zheng-wei ◽  
王建立 WANG Jian-li ◽  
王斌 WANG Bin ◽  
郭爽 GUO Shuang
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Single Pixel

Reflective Single-Pixel Terahertz Imaging Based on Compressed Sensing

2020 ◽  
Vol 10 (5) ◽  
pp. 495-501 ◽  
Author(s):  
Yue Lu ◽  
Xin-Ke Wang ◽  
Wen-Feng Sun ◽  
Sheng-Fei Feng ◽  
Jia-Sheng Ye ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Terahertz Imaging ◽  
Single Pixel

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

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