Publications related to Jump-Sparse and Sparse Recovery Using Potts Functionals

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

On the lower semicontinuous envelope of functionals defined on polyhedral chains

Maria Colombo

In this note we prove an explicit formula for the lower semicontinuous envelope of some functionals defined on real polyhedral chains. More precisely, denoting by H:R→[0,∞) an even, subadditive, and lower semicontinuous function with H(0)=0, and by ΦH the ...

2017

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

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

Sparse molecular image representation

Pascal Frossard, Sofia Karygianni

Sparsity-based models have proven to be very effective in most image processing applications. The notion of sparsity has recently been extended to structured sparsity models where not only the number of components but also their support is important. This ...

Elsevier2016

‪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

Signal structure: from manifolds to molecules and structured sparsity

Sofia Karygianni

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent struct ...

EPFL2015

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Pierre Vandergheynst, Mahdad Hosseini Kamal

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...

Springer-Verlag Berlin2015

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing framework in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on multiple unknown (but sparse) signals and formulate the ...

Institute of Electrical and Electronics Engineers2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

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Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

On the lower semicontinuous envelope of functionals defined on polyhedral chains

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Sparse molecular image representation

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso

Signal structure: from manifolds to molecules and structured sparsity

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Signal structure: from manifolds to molecules and structured sparsity

On the lower semicontinuous envelope of functionals defined on polyhedral chains

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Sparse molecular image representation

Estimation Error of the Constrained Lasso

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography