Publications related to Sparsity averaging for radio-interferometric imaging

Compressive Multiplexing of Ultrasound Signals

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Marcel Arditi, Adrien Georges Jean Besson

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been deve ...

IEEE2018

Compressive Multiplexing of Ultrasound Signals

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Marcel Arditi, Adrien Besson

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been deve ...

IEEE2018

Phaseless Diffraction Tomography With Regularized Beam Propagation

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies, Ferréol Arnaud Marie Soulez, Alexandre Goy, Joowon Lim

In recent years, researchers have obtained impressive reconstructions of the refractive index (RI) of biological objects through the combined use of advanced physics (nonlinear forward model) and regularization. Here, we propose an adaptation of these tech ...

IEEE2018

Sparse inverse problems for Fourier imaging

Anna Auria Rasclosa

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

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

A sparse reconstruction framework for Fourier-based plane wave imaging

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

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared to traditional delay ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

We consider design of sparse controllers for a stochastic linear system with infinite horizon quadratic objective. We formulate the non-sparse optimal solution through a semidefinite program for the second order moments of the states and inputs. Given that ...

University of Minnesota2016

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

Sparsity averaging for radio-interferometric imaging

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Compressive Multiplexing of Ultrasound Signals

Compressive Multiplexing of Ultrasound Signals

Phaseless Diffraction Tomography With Regularized Beam Propagation

Sparse inverse problems for Fourier imaging

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Sparse regularization methods in ultrafast ultrasound imaging

A sparse reconstruction framework for Fourier-based plane wave imaging

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso

Compressive Multiplexing of Ultrasound Signals

Phaseless Diffraction Tomography With Regularized Beam Propagation

Compressive Multiplexing of Ultrasound Signals

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Sparse inverse problems for Fourier imaging

A sparse reconstruction framework for Fourier-based plane wave imaging

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Sparse regularization methods in ultrafast ultrasound imaging

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso