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ICASSP
2009
IEEE
15 years 10 months ago
Compressive sensing for sparsely excited speech signals
Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse respo...
Thippur V. Sreenivas, W. Bastiaan Kleijn
SIGPRO
2010
135views more  SIGPRO 2010»
15 years 2 months ago
A short note on compressed sensing with partially known signal support
This short note studies a variation of the Compressed Sensing paradigm introduced recently by Vaswani et al., i.e. the recovery of sparse signals from a certain number of linear m...
Laurent Jacques
123
Voted
CORR
2010
Springer
167views Education» more  CORR 2010»
15 years 1 months ago
Compressive Sensing over Graphs
In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are ...
Weiyu Xu, Enrique Mallada, Ao Tang
155
Voted
CORR
2011
Springer
282views Education» more  CORR 2011»
14 years 10 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
CORR
2008
Springer
178views Education» more  CORR 2008»
15 years 3 months ago
Model-Based Compressive Sensing
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K N elements from a...
Richard G. Baraniuk, Volkan Cevher, Marco F. Duart...