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Video Compressive Sensing Using Gaussian Mixture Models
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DOI：10.1109/TIP.2014.2344294.png)
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摘要
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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.
Keyword:
Compressive sensing
Gaussian mixture model
online learning
coded aperture compressive temporal imaging (CACTI)
blind compressive sensing
dictionary learning
union-of-subspace model
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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