Publications associées à Acquisition comprimée

Fast Computation Of Spectral Projectors Of Banded Matrices

We consider the approximate computation of spectral projectors for symmetric banded matrices. While this problem has received considerable attention, especially in the context of linear scaling electronic structure methods, the presence of small relative s ...

Siam Publications2017

Compressed imaging by sparse random convolution

Theo Lasser, Aurélien Bourquard, Antonio Lopez

The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interestin ...

Optical Society of America2016

Robust compressive sensing of sparse signals: A review

Alonso Ramirez Manzanares, Rafael Eduardo Carrillo Rangel

Compressive sensing generally relies on the L2-norm for data fidelity, whereas in many applications robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the prese ...

Springer International Publishing Ag2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Volkan Cevher, Jonathan Mark Scarlett

Linear sketching is a powerful tool for the problem of sparse signal recovery, having numerous applications such as compressive sensing, data stream computing, graph sketching, and routing. Motivated by applications where the \emph{positions} of the non-ze ...

2016

Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

In this paper, the problem of speech source localization and separation from recordings of convolutive underdetermined mixtures is studied. The problem is cast as recovering the spatio-spectral speech information embedded in a microphone array compressed m ...

Elsevier Science Bv2016

A compressed beamforming framework for ultrafast ultrasound imaging

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

Classical beamforming methods, based on Delay- And-Sum (DAS) require an extensive number of samples and delay calculations to obtain high-quality images. Compressed Beamforming (CB) proposes an alternative to DAS, based on compressed sensing, which aims at ...

Ieee2016

Sparse regularization methods in ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Adrien Georges Jean Besson, Olivier Bernard

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstructio ...

Ieee2016

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Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Acquisition comprimée

Fast Computation Of Spectral Projectors Of Banded Matrices

Compressed imaging by sparse random convolution

Robust compressive sensing of sparse signals: A review

Estimation Error of the Constrained Lasso

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

A compressed beamforming framework for ultrafast ultrasound imaging

Sparse regularization methods in ultrafast ultrasound imaging

Graph Chatbot

Chattez avec Graph Search

Compressed imaging by sparse random convolution

Fast Computation Of Spectral Projectors Of Banded Matrices

Robust compressive sensing of sparse signals: A review

Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

A compressed beamforming framework for ultrafast ultrasound imaging

Sparse regularization methods in ultrafast ultrasound imaging

Estimation Error of the Constrained Lasso

‪Stochastic Control-A Moment Approach to Sparse Control Design‬