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CORR
2007
Springer
183views Education» more  CORR 2007»
14 years 9 months ago
Compressed Sensing and Redundant Dictionaries
This article extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, whic...
Holger Rauhut, Karin Schnass, Pierre Vandergheynst
NIPS
2008
14 years 11 months 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...
ICASSP
2011
IEEE
14 years 1 months ago
Lorentzian based iterative hard thresholding for compressed sensing
In this paper we propose a robust iterative hard thresolding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use ...
Rafael E. Carrillo, Kenneth E. Barner
CORR
2011
Springer
282views Education» more  CORR 2011»
14 years 4 months ago
Fast Linearized Bregman Iteration for Compressive Sensing and Sparse Denoising
We propose and analyze an extremely fast, efficient and simple method for solving the problem: min{ u 1 :Au=f,u∈Rn }. This method was first described in [1], with more details i...
Stanley Osher, Yu Mao, Bin Dong, Wotao Yin
TIT
2010
174views Education» more  TIT 2010»
14 years 4 months ago
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation
Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few ...
Jarvis Haupt, Waheed Uz Zaman Bajwa, Gil M. Raz, R...