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Nonconvex compressive video sensing

[+] Author Affiliations
Liangliang Chen, Zhanxin Zhou, Yongliang Yang, Bo Song, Lixin Dong

Michigan State University, Department of Electrical and Computer Engineering, East Lansing, Michigan 48824, United States

Ming Yan

Michigan State University, Department of Computational Mathematics, Science and Engineering, East Lansing, Michigan 48824, United States

Michigan State University, Department of Mathematics, East Lansing, Michigan 48824, United States

Chunqi Qian

Michigan State University, Department of Radiology, East Lansing, Michigan 48824, United States

Ning Xi

The University of Hong Kong, Department of Industrial and Manufacturing Systems Engineering, Hong Kong

J. Electron. Imaging. 25(6), 063003 (Nov 15, 2016). doi:10.1117/1.JEI.25.6.063003
History: Received May 2, 2016; Accepted October 20, 2016
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Abstract.  High-speed cameras explore more details than normal cameras in the time sequence, while the conventional video sampling suffers from the trade-off between temporal and spatial resolutions due to the sensor’s physical limitation. Compressive sensing overcomes this obstacle by combining the sampling and compression procedures together. A single-pixel-based real-time video acquisition is proposed to record dynamic scenes, and a fast nonconvex algorithm for the nonconvex sorted 1 regularization is applied to reconstruct frame differences using few numbers of measurements. Then, an edge-detection-based denoising method is employed to reduce the error in the frame difference image. The experimental results show that the proposed algorithm together with the single-pixel imaging system makes compressive video cameras available.

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© 2016 SPIE and IS&T

Citation

Liangliang Chen ; Ming Yan ; Chunqi Qian ; Ning Xi ; Zhanxin Zhou, et al.
"Nonconvex compressive video sensing", J. Electron. Imaging. 25(6), 063003 (Nov 15, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.063003


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