Video compressive sensing using gaussian mixture models

Jianbo Yang, Xin Yuan, Xuejun Liao, Patrick Llull, David J. Brady, Guillermo Sapiro, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

165 Scopus citations

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.

Original languageEnglish (US)
Article number2344294
Pages (from-to)4863-4878
Number of pages16
JournalIEEE Transactions on Image Processing
Volume23
Issue number11
DOIs
StatePublished - Nov 1 2014
Externally publishedYes

Keywords

  • Blind compressive sensing
  • Coded aperture compressive temporal imaging (CACTI)
  • Compressive sensing
  • Dictionary learning
  • Gaussian mixture model
  • Online learning
  • Union-of-subspace model

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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