Kamilov, UlugbekAmini, ArashUnser, Michael2013-03-282013-03-282013-03-28201210.1109/ICASSP.2012.6288704https://infoscience.epfl.ch/handle/20.500.14299/90627WOS:000312381403177Many 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.signal denoisingsparse estimationTV denoisingtext::conference output::conference proceedings::conference paper