Publications by Karin Schnass | EPFL Graph Search

Compressed sensing and redundant dictionaries

This article extends the concept of it compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic ...

2008

Average Performance Analysis for Thresholding

Pierre Vandergheynst, Karin Schnass

In this article is shown that with high probability the thresholding algorithm can recover signals that are sparse in a redundant dictionary as long as the {\it 2-Babel function} is growing slowly. This implies that it can succeed for sparsity levels up to ...

2007

Average case analysis of multichannel sparse approximations using p- thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces

p

-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latte ...

2007

Multichannel thresholding with sensing dictionaries

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this ...

2007

Average case analysis of multichannel sparse approximations using p-thresholding - art. no. 67011X

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2007

Average case analysis of multichannel thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter ...

2007

Distributed sensing of noisy signals by thresholding of redundant expansions

Pascal Frossard, Pierre Vandergheynst, Karin Schnass

This paper addresses the problem of sensing or recovering a signal s, captured by distributed low-complexity sensors. Each sensor observes a noisy version of the signal of interest, and independently forms an approximant of its observation. This approximan ...

2006

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Compressed sensing and redundant dictionaries

Average Performance Analysis for Thresholding

Average case analysis of multichannel sparse approximations using p- thresholding

Multichannel thresholding with sensing dictionaries

Average case analysis of multichannel sparse approximations using p-thresholding - art. no. 67011X

Average case analysis of multichannel thresholding

Distributed sensing of noisy signals by thresholding of redundant expansions

Compressed sensing and redundant dictionaries

Multichannel thresholding with sensing dictionaries

Average case analysis of multichannel sparse approximations using p- thresholding

Average Performance Analysis for Thresholding

Average case analysis of multichannel thresholding

Average case analysis of multichannel sparse approximations using p-thresholding - art. no. 67011X

Distributed sensing of noisy signals by thresholding of redundant expansions