Sublinear Recovery of Sparse Wavelet Signals

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Sublinear Recovery of Sparse Wavelet Signals
There are two main classes of decoding algorithms for "compressed sensing," those which run time time polynomial in the signal length and those which use sublinear resources. Most of the sublinear algorithms focus on signals which are compressible in either the Euclidean domain or the Fourier domain. Unfortunately, most practical signals are not sparse in either one of these domains. Instead, they are sparse (or nearly so) in the Haar wavelet system. We present a modified sublinear recovery algorithm which utilizes the recursive structure of Reed-Muller codes to recover a wavelet-sparse signal from a small set of pseudo-random measurements. We also discuss an implementation of the algorithm to illustrate proof-of-concept and empirical analysis.
Ray Maleh, Anna C. Gilbert
Added 25 Dec 2009
Updated 25 Dec 2009
Type Conference
Year 2008
Where DCC
Authors Ray Maleh, Anna C. Gilbert
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