@inproceedings{a786db4978684182a795cd3b407bce45,
title = "Low density frames for compressive sensing",
abstract = "We consider the compressive sensing of a sparse or compressible signal x ∈ ℝM. We explicitly construct a class of measurement matrices, referred to as the low density frames, and develop decoding algorithms that produce an accurate estimate {\^x} even in the presence of additive noise. Low density frames are sparse matrices and have small storage requirements. Our decoding algorithms for these frames can be implemented in O(Mdvdc) complexity, where dc and dv are the row and column weight of the frame respectively. Simulation results are provided, demonstrating that our approach significantly outperforms state-of-the-art recovery algorithms for numerous cases of interest.",
keywords = "Compressive sensing, Gaussian scale mixtures, Low density frames",
author = "Mehmet Ak{\c c}akaya and Jinsoo Park and Vahid Tarokh",
year = "2010",
doi = "10.1109/ICASSP.2010.5495898",
language = "English (US)",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3642--3645",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}