Publications related to Compressed sensing

Sparse inverse problems for Fourier imaging

Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain. The recent theory of Compressed Sensing exploits this property offering a powerful frame ...

EPFL2017

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson, Florian Martinez

Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution of the image reconstruction problem. An alternative to DAS consists in using iterative techniques which require both an a ...

2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Volkan Cevher, Jonathan Mark Scarlett

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings, such as compressive sensing, subset selection in regression, and gro ...

Institute of Electrical and Electronics Engineers2017

A compressed-sensing approach for ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson

Ultrasonography uses multiple piezo-electric element probes to image tissues. Current time-domain beamforming techniques require the signal at each transducer-element to be sampled at a rate higher than the Nyquist criterion, resulting in an extensive amou ...

2017

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Michaël Unser, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies

Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model (Born or Rytov), which limits their applicability to t ...

2017

Graph learning under sparsity priors

Pascal Frossard, Hermina Petric Maretic

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Ieee2017

Learning the weight matrix for sparsity averaging in compressive imaging

Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson

We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal wei ...

2017

Dictionary Learning Based on Sparse Distribution Tomography

Michaël Unser, Pedram Pad

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transfo ...

P. Pad2017

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Sparse inverse problems for Fourier imaging

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Acoustic DoA Estimation by One Unsophisticated Sensor

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

A compressed-sensing approach for ultrasound imaging

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Graph learning under sparsity priors

Learning the weight matrix for sparsity averaging in compressive imaging

Dictionary Learning Based on Sparse Distribution Tomography

Acoustic DoA Estimation by One Unsophisticated Sensor

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Learning the weight matrix for sparsity averaging in compressive imaging

Dictionary Learning Based on Sparse Distribution Tomography

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Sparse inverse problems for Fourier imaging

A compressed-sensing approach for ultrasound imaging

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Graph learning under sparsity priors