Mmse Denoising Of Sparse Levy Processes Via Message Passing

Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total-variation (TV) minimization, implicitly assume the signals to be sampled instances of Levy processes with independent Laplace-distributed increments. By extending the concept to more general Levy processes, we propose an efficient minimum-mean-squared error (MMSE) estimation method based on message-passing algorithms on factor graphs. The resulting algorithm can be used to benchmark the performance of the existing or design new algorithms for the recovery of sparse signals.


Published in:
2012 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 3637-3640
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, JAPAN, MAR 25-30, 2012
Year:
2012
Publisher:
New York, Ieee
ISBN:
978-1-4673-0046-9
Keywords:
Laboratories:




 Record created 2013-03-28, last modified 2018-03-17

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