Gaussian mixture model for video compressive sensing

TitleGaussian mixture model for video compressive sensing
Publication TypeJournal Article
Year of Publication2013
AuthorsJ Yang, X Yuan, X Liao, P Llull, G Sapiro, DJ Brady, and L Carin
Journal2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings
Start Page19
Pagination19 - 23
Date Published01/2013
Abstract

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.

DOI10.1109/ICIP.2013.6738005
Short Title2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings