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14 May 2015 Compressive sensing for noisy video reconstruction
Huihuang Zhao, John Montalbo, Shuxia Li, Yaqi Sun, Zhijun Qiao
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Proceedings Volume 9484, Compressive Sensing IV; 94840C (2015) https://doi.org/10.1117/12.2180358
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States
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, John Montalbo, Shuxia Li, Yaqi Sun, and Zhijun Qiao "Compressive sensing for noisy video reconstruction", Proc. SPIE 9484, Compressive Sensing IV, 94840C (14 May 2015); https://doi.org/10.1117/12.2180358
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KEYWORDS
Video
Reconstruction algorithms
Compressed sensing
Video compression
Interference (communication)
Signal to noise ratio
Video processing
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