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ICASSP
2011
IEEE
13 years 2 months ago
Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals
In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly spa...
Shubha Shedthikere, Ananthanarayanan Chockalingam
ICASSP
2011
IEEE
13 years 2 months ago
Bayesian Compressive Sensing for clustered sparse signals
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
Lei Yu, Hong Sun, Jean-Pierre Barbot, Gang Zheng
NIPS
2008
14 years 10 days ago
Sparse Signal Recovery Using Markov Random Fields
Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the th...
Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Ric...
CIMAGING
2008
142views Hardware» more  CIMAGING 2008»
14 years 11 days ago
Greedy signal recovery and uncertainty principles
This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery from an incomplete set of linear measurements
Deanna Needell, Roman Vershynin
ICASSP
2008
IEEE
14 years 5 months ago
Distributed compressed sensing: Sparsity models and reconstruction algorithms using annihilating filter
Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some commo...
Ali Hormati, Martin Vetterli
ICASSP
2008
IEEE
14 years 5 months ago
Iteratively reweighted algorithms for compressive sensing
The theory of compressive sensing has shown that sparse signals can be reconstructed exactly from many fewer measurements than traditionally believed necessary. In [1], it was sho...
Rick Chartrand, Wotao Yin
ICASSP
2009
IEEE
14 years 5 months ago
RLS-weighted Lasso for adaptive estimation of sparse signals
The batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for estimating sparse signals of interest emerging in various applications, where obse...
Daniele Angelosante, Georgios B. Giannakis
ICIP
2008
IEEE
15 years 19 days ago
Kalman filtered Compressed Sensing
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...
Namrata Vaswani