Gaussian mixture model for video compressive sensing

A Gaussian Mixture Model (GMM)-based algorithm is proposed for video reconstruction from temporal compressed measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The developed GMM reconstruction method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed GMM with videos reconstructed from simulated compressive video measurements and from a real compressive video camera. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Yang, J; Yuan, X; Liao, X; Llull, PR; Sapiro, GR; Brady, DJ; Carin, L

Published Date

  • December 1, 2013

Published In

  • 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Start / End Page

  • 19 - 23

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2013.6738005

Citation Source

  • Scopus