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Proceedings Paper

Compressive sensing for noisy video reconstruction
Author(s): Huihuang Zhao; John Montalbo; Shuxia Li; Yaqi Sun; Zhijun Qiao
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Paper Abstract

In this paper, a compressing and reconstruction method for a noise video based on Compressed Sensing (CS) theory is proposed. At first, the CS theory is presented. Then the noise video is estimated from noisy measurement by solving the convex minimization problem. The video recovery algorithms based on gradient-based method is used to compressing and reconstructing the noise signal. And a compressive sensing algorithm with gradient-based method is proposed. At last, the performance of the proposed approach is shown and compared with some conventional algorithms. Our method can obtain best results in terms of peak signal noise ratio (PSNR) than those achieved by common methods with only a little runtime.

Paper Details

Date Published: 14 May 2015
PDF: 10 pages
Proc. SPIE 9484, Compressive Sensing IV, 94840C (14 May 2015); doi: 10.1117/12.2180358
Show Author Affiliations
Huihuang Zhao, Hengyang Normal Univ. (China)
The Univ. of Texas-Pan American (United States)
John Montalbo, The Univ. of Texas-Pan American (United States)
Shuxia Li, The Univ. of Texas-Pan American (United States)
Yaqi Sun, Hengyang Normal Univ. (China)
Zhijun Qiao, The Univ. of Texas-Pan American (United States)


Published in SPIE Proceedings Vol. 9484:
Compressive Sensing IV
Fauzia Ahmad, Editor(s)

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