Publications related to Compressed sensing

Compressive Sensing For Sensor Calibration

We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor ...

2008

Dynamic PET Reconstruction Using Wavelet Regularization with Adapted Basis Functions

Michaël Unser, Dimitri Nestor Alice Van De Ville, Ildar Khalidov

Tomographic reconstruction from positron emission tomography (PET) data is an ill-posed problem that requires regularization. An attractive approach is to impose an

\ell_{ 1 }

-regularization constraint, which favors sparse solutions in the wavelet dom ...

Institute of Electrical and Electronics Engineers2008

Fast keyword detection with sparse time-frequency models

Pascal Frossard, Olivier Verscheure, Effrosyni Kokiopoulou

We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on sparse representation of speech signals in a time-frequency feature space. The t ...

2008

Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper provides new results on computing simultaneous sparse approximations of multichannel signals over redundant dictionaries using two greedy algorithms. The first one, p-thresholding, selects the S atoms that have the largest

p

-correlation while ...

2008

Dictionary Identifiability from Few Training Samples

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

ell^1

minimisation, or more precisely the problem of identifying a dictionary

dico

from a set of training samples

Y

knowing that $ ...

2008

Differences between observation and sampling error in sparse signal reconstruction

Michael Christoph Gastpar

The field of Compressed Sensing has shown that a relatively small number of random projections provide sufficient information to accurately reconstruct sparse signals. Inspired by applications in sensor networks in which each sensor is likely to observe a ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Annihilating filter-based decoding in the compressed sensing framework - art. no. 670121

Martin Vetterli, Ali Hormati

Recent results in compressed sensing or compressive sampling suggest that a relatively small set of measurements taken as the inner product with universal random measurement vectors can well represent a source that is sparse in some fixed basis. By adaptin ...

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

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

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

On finding nearest neighbors in a set of compressible signals

Pierre Vandergheynst, Philippe Jost

Numerous applications demand that we manipulate large sets of very high-dimensional signals. A simple yet common example is the problem of finding those signals in a database that are closest to a query. In this paper, we tackle this problem by restricting ...

2007

Compressed sensing

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Compressive Sensing For Sensor Calibration

Dynamic PET Reconstruction Using Wavelet Regularization with Adapted Basis Functions

Fast keyword detection with sparse time-frequency models

Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms

Dictionary Identifiability from Few Training Samples

Differences between observation and sampling error in sparse signal reconstruction

Annihilating filter-based decoding in the compressed sensing framework - art. no. 670121

Average Performance Analysis for Thresholding

Multichannel thresholding with sensing dictionaries

On finding nearest neighbors in a set of compressible signals

Annihilating filter-based decoding in the compressed sensing framework - art. no. 670121

Dynamic PET Reconstruction Using Wavelet Regularization with Adapted Basis Functions

Fast keyword detection with sparse time-frequency models

Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms

Dictionary Identifiability from Few Training Samples

Differences between observation and sampling error in sparse signal reconstruction

Average Performance Analysis for Thresholding

Multichannel thresholding with sensing dictionaries

On finding nearest neighbors in a set of compressible signals

Compressive Sensing For Sensor Calibration