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- W2028339660 abstract "A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression." @default.
- W2028339660 created "2016-06-24" @default.
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- W2028339660 date "2014-11-01" @default.
- W2028339660 modified "2023-10-10" @default.
- W2028339660 title "Video Compressive Sensing Using Gaussian Mixture Models" @default.
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- W2028339660 doi "https://doi.org/10.1109/tip.2014.2344294" @default.
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