MMSE denoising of sparse Lévy 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 assum...

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Hauptverfasser:	Kamilov, U., Amini, A., Unser, M.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithm design and analysis Estimation Fourier transforms Frequency domain analysis Noise Noise reduction signal denoising sparse estimation TV denoising
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creator	Kamilov, U. Amini, A. Unser, M.
description	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 Lévy processes with independent Laplace-distributed increments. By extending the concept to more general Lévy 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.
doi_str_mv	10.1109/ICASSP.2012.6288704
format	Conference Proceeding
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subjects	Algorithm design and analysis Estimation Fourier transforms Frequency domain analysis Noise Noise reduction signal denoising sparse estimation TV denoising
title	MMSE denoising of sparse Lévy processes via message passing
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