14 May 2015 Compressive sensing for noisy video reconstruction
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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.
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Huihuang Zhao, Huihuang Zhao, John Montalbo, John Montalbo, Shuxia Li, Shuxia Li, Yaqi Sun, Yaqi Sun, Zhijun Qiao, Zhijun Qiao, } "Compressive sensing for noisy video reconstruction", Proc. SPIE 9484, Compressive Sensing IV, 94840C (14 May 2015); doi: 10.1117/12.2180358; https://doi.org/10.1117/12.2180358
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