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

Sai Qi Shang; Min Gang Wang; Wei Li; Yao Yang

doi:10.4028/www.scientific.net/AMR.756-759.3785

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759A Terahertz Compressive Imaging Method Based on...

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

Abstract:

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.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

3785-3788

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.3785

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Online since:

September 2013

Authors:

Sai Qi Shang, Min Gang Wang, Wei Li, Yao Yang

Keywords:

Compressed Sensing, Measurement Matrix, Random Template, Single Detector, Terahertz Imaging

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References

[1] Wai Lam Chan, Kriti Charan, Dharmpal Takhar, Kevin F. Kelly, Richard G. Baraniuk, and Daniel M. Mittleman. A single-pixel terahertz imaging system based on compressed sensing [J]. Applied Physcs letters 93, (2008).

DOI: 10.1063/1.2989126

Google Scholar

[2] Lishu Tao, Wei Dan. Compressed sensing overview [J]. AAS, (2009).

Google Scholar

[3] Li Wei. Terahertz imaging system based on compressed sensing of single-detector [D]. (2012).

Google Scholar

[4] Zhao Juxin. Imaging system based on compressed sensing of single-detector [D]. (2011).

Google Scholar

[5] D. Needell and R. Vershynin. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit [J]. IEEE J Select Top Signal Processing, 2010, 4(2) 310-316.

DOI: 10.1109/jstsp.2010.2042412

Google Scholar

[6] D. Needell and J. Tropp. Iterative signal recovery from incomplete and inaccurate samples [J]. Appl Comput Harmon Anal, 2009, 26(3) 301-321.

DOI: 10.1016/j.acha.2008.07.002

Google Scholar

[7] Emmanuel Candés and Justin Romberg, Caltech. L1-magic: Recovery of Sparse Signals via Convex Programming [J]. (2005).

Google Scholar

[8] Zhang Cunlin, Mu Kaijun. THz spectrum and imaging [J]. Laser and Optoelectronics Process. (2007).

Google Scholar