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

Jump-Sparse and Sparse Recovery Using Potts Functionals

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Metric Learning with Rank and Sparsity Constraints

Volkan Cevher, Baran Gözcü, Bubacarr Bah

Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...

Ieee2014

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

Joachim Krieger

For the radial energy-supercritical nonlinear wave equation

\Box u = -u_{tt} + \triangle u = \pm u^7

on

\R^{3+1}

, we prove the existence of a class of global in forward time

C^\infty

-smooth solutions with infinite critical Sobolev norm $\dot{H}^{\f ...

Springer Verlag2014

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupt ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Sparse Distributed Learning Based on Diffusion Adaptation

Ali H. Sayed

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the d ...

IEEE2013

On Sparsity Averaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

Recent developments in [1] and [2] introduced a novel regularization method for compressive imaging in the con- text of compressed sensing with coherent redundant dictionaries. The approach relies on the observation that natural images exhibit strong avera ...

2013

Sparsity averaging for radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

We propose a novel regularization method for compressive imaging in the context of the CS theory with coherent and redundant dictionaries. The approach relies on the conjecture that natural images exhibit strong average sparsity over multiple coherent fram ...

2013

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Inverse problems with shot noise arise in many modern biomedical imaging applications. The main challenge is to obtain an estimate of the underlying specimen from measurements corrupted by Poisson noise. In this work, we propose an efficient framework for ...

IEEE2013

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Jean-Philippe Thiran, Pierre Vandergheynst, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Gilles Puy, Jason Douglas McEwen

A new sampling theorem on the sphere has been developed recently, reducing the number of samples required to represent a band-limited signal by a factor of two for equiangular sampling schemes. For signals sparse in a spatially localised measure, such as i ...

Institute of Electrical and Electronics Engineers2013

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequenti ...

Springer2013

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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Jump-Sparse and Sparse Recovery Using Potts Functionals

Metric Learning with Rank and Sparsity Constraints

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Sparse Distributed Learning Based on Diffusion Adaptation

On Sparsity Averaging

Sparsity averaging for radio-interferometric imaging

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Sparse image reconstruction on the sphere: implications of a new sampling theorem

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

On Sparsity Averaging

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsity averaging for radio-interferometric imaging

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Sparse Distributed Learning Based on Diffusion Adaptation

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Metric Learning with Rank and Sparsity Constraints

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$