Publications related to Sparse Approximation by Linear Programming using an L1 Data-Fidelity Term

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Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

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

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-spa ...

2019

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

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

Sparsity in tensor optimization for optical-interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Anna Auria Rasclosa

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bispectrum measurements. We review our previous work, which reformulates this nonlinear problem in the framework of tensor recover ...

IEEE2014

Sparse projections onto the simplex

Volkan Cevher, Christoph Koch, Anastasios Kyrillidis

Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the`1-norm. However, several important learning applications cannot benet from this approach as they feature these convex no ...

JMLR W&CP2013

Bearing estimation via spatial sparsity using compressive sensing

Volkan Cevher

Bearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, we propose a method to specifically exploit this spatial sparsity property. The method us ...

Institute of Electrical and Electronics Engineers2012

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Pascal Frossard, Pierre Vandergheynst, Adel Rahmoune

This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...

Institute of Electrical and Electronics Engineers2012

Hard Thresholding with Norm Constraints

Volkan Cevher, Gilles Puy, Anastasios Kyrillidis

We introduce a new sparse recovery paradigm, called Normed Pursuits, where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions. Synthetic and real data experiments illustrate that Normed Pur ...

Ieee2012

On Accelerated Hard Thresholding Methods for Sparse Approximation

Volkan Cevher

We propose and analyze acceleration schemes for hard thresholding methods with applications to sparse approximation in linear inverse systems. Our acceleration schemes fuse combinatorial, sparse projection algorithms with convex optimization algebra to pro ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2011

Sparse Approximation by Linear Programming using an L1 Data-Fidelity Term

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Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

A totally unimodular view of structured sparsity

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsity in tensor optimization for optical-interferometric imaging

Sparse projections onto the simplex

Bearing estimation via spatial sparsity using compressive sensing

Compressive Sampling Strategies for Multichannel Signals

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Hard Thresholding with Norm Constraints

On Accelerated Hard Thresholding Methods for Sparse Approximation

On Accelerated Hard Thresholding Methods for Sparse Approximation

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Jump-Sparse and Sparse Recovery Using Potts Functionals

A totally unimodular view of structured sparsity

Bearing estimation via spatial sparsity using compressive sensing

Sparse projections onto the simplex

Compressive Sampling Strategies for Multichannel Signals

Sparsity in tensor optimization for optical-interferometric imaging

Hard Thresholding with Norm Constraints