@inproceedings{Michael2013a,
abstract = {Compressive Sensing (CS) suggests that, under certain conditions, a signal can be reconstructed using a small number of incoherent measurements. We propose a novel video CS framework based on Multiple Measurement Vectors (MMV) which is suitable for signals with temporal correlation such as video sequences. In addition, a CS circulant matrix is employed for fast reconstruction. Furthermore, the proposed framework allows the number of CS measurements associated with each frame to be chosen in the decoder rather than the encoder offering robustness compared to the multi-scale approaches. Experimental results on two video sequences exhibiting fast motion and occlusions, show the advantages of the proposed method over the current state-of-the-art in video CS. {\textcopyright} 2013 IEEE.},
author = {Iliadis, Michael and Watt, Jeremy and Spinoulas, Leonidas and Katsaggelos, Aggelos K.},
booktitle = {2013 IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2013.6738029},
isbn = {978-1-4799-2341-0},
keywords = {Video compressive sensing,circulant matrix,fast motion,multiple measurement vectors},
month = {sep},
pages = {136--140},
publisher = {IEEE},
title = {{Video compressive sensing using multiple measurement vectors}},
url = {http://ieeexplore.ieee.org/document/6738029/},
year = {2013}
}

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