Publications related to Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Differential Phase-Contrast x-Ray Computed Tomography: From Model Discretization to Image Reconstruction

Our contribution in this paper is two fold. First, we propose a novel discretization of the forward model for differential phase-contrast imaging that uses B-spline basis functions. The approach yields a fast and accurate algorithm for implementing the fo ...

IEEE2012

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Jean-Charles Baritaux

The recent advances in fluorescent molecular probes, photon detection instrumentation, and photon propagation models in tissue, have facilitated the emergence of innovative molecular imaging technologies such as Fluorescence Diffuse Optical Tomography (FDO ...

EPFL2012

Distributed Compressed Representation of Correlated Image Sets

Vijayaraghavan Thirumalai

Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoin ...

EPFL2012

The importance of priors for l2 regularization and total variation methods in quantitative susceptibility mapping

Rolf Gruetter, Yves Wiaux, José Pedro Rebelo Ferreira Marques, Gilles Puy, Diana Khabipova

Phase imaging has been demonstrated to offer a good contrast between and within brain tissues at 7T[1] with iron and myelin concentration being amongst the main modulators of the observed contrast due to their para- and dia-magnetic properties.Phase imagin ...

2012

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...

Institute of Electrical and Electronics Engineers2012

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional ...

Ieee2012

Hessian-Based Regularization for 3-D Microscopy Image Restoration

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functiona ...

IEEE2012

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

Sparse diffusion LMS for distributed adaptive estimation

Ali H. Sayed

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...

IEEE2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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Differential Phase-Contrast x-Ray Computed Tomography: From Model Discretization to Image Reconstruction

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Distributed Compressed Representation of Correlated Image Sets

The importance of priors for l2 regularization and total variation methods in quantitative susceptibility mapping

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Hessian-Based Regularization for 3-D Microscopy Image Restoration

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Sparse diffusion LMS for distributed adaptive estimation

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Distributed Compressed Representation of Correlated Image Sets

Sparse diffusion LMS for distributed adaptive estimation

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Differential Phase-Contrast x-Ray Computed Tomography: From Model Discretization to Image Reconstruction

The importance of priors for l2 regularization and total variation methods in quantitative susceptibility mapping

Hessian-Based Regularization for 3-D Microscopy Image Restoration

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices