Open Access
15 November 2016 Nonconvex compressive video sensing
Liangliang Chen, Ming Yan, Chunqi Qian, Ning Xi, Zhanxin Zhou, Yongliang Yang, Bo Song, Lixin Dong
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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.
© 2016 SPIE and IS&T 1017-9909/2016/$25.00 © 2016 SPIE and IS&T
Liangliang Chen, Ming Yan, Chunqi Qian, Ning Xi, Zhanxin Zhou, Yongliang Yang, Bo Song, and Lixin Dong "Nonconvex compressive video sensing," Journal of Electronic Imaging 25(6), 063003 (15 November 2016). https://doi.org/10.1117/1.JEI.25.6.063003
Published: 15 November 2016
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Video

Video compression

Denoising

Compressed sensing

Cameras

Reconstruction algorithms

Image restoration


CHORUS Article. This article was made freely available starting 15 November 2017

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