Publications related to Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

Non-local Functionals Related to the Total Variation and Connections with Image Processing

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018

A Fourier dimensionality reduction model for big data interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Vijay Kartik

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging method ...

Oxford Univ Press2017

MRI artifact correction using sparse plus low-rank decomposition of annihilating filter-based hankel matrix

Kyong Hwan Jin

PurposeMagnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as ir ...

Wiley2017

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

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

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

Structured Sparse Coding for Image Denoising or Pattern Detection

Pascal Frossard, Sofia Karygianni

Sparsity has been one of the major drives in signal processing in the last decade. Structured sparsity has also lately emerged as a way to enrich signal priors towards more meaningful and accurate representations. In this paper we propose a new structured ...

2014

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 ...

2014

Equivalence of synthesis and atomic formulations of sparse recovery

Volkan Cevher, Mitra Fatemi, Shayan Dashmiz

Recovery of sparse signals from linear, dimensionality reducing measurements broadly fall under two well-known formulations, named the synthesis and the analysis a ́ la Elad et al. Recently, Chandrasekaran et al. introduced a new algorithmic sparse recover ...

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

Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

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Non-local Functionals Related to the Total Variation and Connections with Image Processing

A Fourier dimensionality reduction model for big data interferometric imaging

MRI artifact correction using sparse plus low-rank decomposition of annihilating filter-based hankel matrix

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

Signal structure: from manifolds to molecules and structured sparsity

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Structured Sparse Coding for Image Denoising or Pattern Detection

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

Equivalence of synthesis and atomic formulations of sparse recovery

On Accelerated Hard Thresholding Methods for Sparse Approximation

Non-local Functionals Related to the Total Variation and Connections with Image Processing

A Fourier dimensionality reduction model for big data interferometric imaging

Signal structure: from manifolds to molecules and structured sparsity

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

MRI artifact correction using sparse plus low-rank decomposition of annihilating filter-based hankel matrix

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

On Accelerated Hard Thresholding Methods for Sparse Approximation

Structured Sparse Coding for Image Denoising or Pattern Detection

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Equivalence of synthesis and atomic formulations of sparse recovery