@inproceedings{8b560fb98f5c436aa766b02d667d6764,
title = "Gaussian mixture model for video compressive sensing",
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.",
keywords = "Compressive sensing, Gaussian mixture model, coded aperture compressive temporal imaging",
author = "Jianbo Yang and Xin Yuan and Xuejun Liao and Patrick Llull and Guillermo Sapiro and Brady, {David J.} and Lawrence Carin",
year = "2013",
doi = "10.1109/ICIP.2013.6738005",
language = "English (US)",
isbn = "9781479923410",
series = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
publisher = "IEEE Computer Society",
pages = "19--23",
booktitle = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",
note = "2013 20th IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",
}