@article{ yang_video_2014,
  title = {Video {Compressive} {Sensing} {Using} {Gaussian} {Mixture} {Models}},
  volume = {23},
  issn = {1057-7149},
  doi = {10.1109/TIP.2014.2344294},
  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.},
  number = {11},
  journal = {IEEE Transactions on Image Processing},
  author = {Yang, J. and Yuan, X. and Liao, X. and Llull, P. and Brady, D.J. and Sapiro, G. and Carin, L.},
  month = {November},
  year = {2014},
  note = {00011},
  pages = {4863--4878}
}

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