Publications related to Compressed sensing

Distributed Compressed Sensing for Sensor Networks Using Thresholding

Pierre Vandergheynst, Mohammad Golbabaee

Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at a central decoder (base station) that exploits the inter-sensor correlations, in order to recover the whole ...

2009

Linear Compressive Networks

Michael Christoph Gastpar, Naveen Goela

A linear compressive network (LCN) is defined as a graph of sensors in which each encoding sensor compresses incoming jointly Gaussian random signals and transmits (potentially) low-dimensional linear projections to neighbors over a noisy uncoded channel. ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Sparsity & [and] dictionaries - algorithms & [and] design

Karin Schnass

With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...

EPFL2009

Basis Identification from Random Sparse 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. The problem is to identify a dictionary

\dico

from a set of training samples

\Y

knowing that

\Y = \dico \X

...

2009

A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

Pierre Vandergheynst, Rémi Gribonval

We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift- invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves a ...

2009

Time Resolved Fluorescence Diffuse Optical Tomography Using Multi-Resolution Exponential B-Splines

Michaël Unser, Chandra Sekhar Seelamantula

This paper deals with the problem of time-resolved fluorescence diffuse optical tomography. We propose a new reconstruction scheme based on a multi-resolution approximation of the time-resolved signals. The underlying basis functions are exponential B-spli ...

IEEE2009

On unifying sparsity and geometry for image-based 3D scene representation

Ivana Tosic

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providin ...

EPFL2009

Integrated Modeling And Reconstruction With Sparsity Constraints For fDOT

Michaël Unser, Matthieu Guerquin-Kern, Jean-Charles Baritaux

We present a numerical framework for Fluorescence Diffuse Optical Tomography (fDOT) that combines a forward model together with an iterative reconstruction procedure. Using rapid linear solvers, we derived an efficient reconstruction strategy for quadratic ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Integrated Modeling and Reconstruction with Sparsity Constraints for fDOT

Michaël Unser, Matthieu Guerquin-Kern, Jean-Charles Baritaux

We present a numerical framework for Fluorescence Diffuse Optical Tomography (fDOT) that combines a forward model together with an iterative reconstruction procedure. Using rapid linear solvers, we derived an efficient reconstruction strategy for quadrati ...

IEEE2009

Conditions for recovery of sparse signals correlated by local transforms

Pascal Frossard, Ivana Tosic

This paper addresses the problem of correct recovery of multiple sparse correlated signals using distributed thresholding. We consider the scenario where multiple sensors capture the same event, but observe different signals that are correlated by local tr ...

2009

Compressed sensing

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Distributed Compressed Sensing for Sensor Networks Using Thresholding

Linear Compressive Networks

Sparsity & [and] dictionaries - algorithms & [and] design

Basis Identification from Random Sparse Samples

A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

Time Resolved Fluorescence Diffuse Optical Tomography Using Multi-Resolution Exponential B-Splines

On unifying sparsity and geometry for image-based 3D scene representation

Integrated Modeling And Reconstruction With Sparsity Constraints For fDOT

Integrated Modeling and Reconstruction with Sparsity Constraints for fDOT

Conditions for recovery of sparse signals correlated by local transforms

A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

Distributed Compressed Sensing for Sensor Networks Using Thresholding

Linear Compressive Networks

Basis Identification from Random Sparse Samples

Sparsity & [and] dictionaries - algorithms & [and] design

On unifying sparsity and geometry for image-based 3D scene representation

Time Resolved Fluorescence Diffuse Optical Tomography Using Multi-Resolution Exponential B-Splines

Integrated Modeling And Reconstruction With Sparsity Constraints For fDOT

Integrated Modeling and Reconstruction with Sparsity Constraints for fDOT

Conditions for recovery of sparse signals correlated by local transforms