Publication

Sparsity & [and] dictionaries - algorithms & [and] design

Publications associées (126)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

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

Learning Parametric Dictionaries for Signals on Graphs

Pascal Frossard, David Shuman

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties – the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on wei ...

Institute of Electrical and Electronics Engineers2014

Dictionary learning over large distributed models via dual-ADMM strategies

Ali H. Sayed

We consider the problem of dictionary learning over large scale models, where the model parameters are distributed over a multi-agent network. We demonstrate that the dual optimization problem for inference is better conditioned than the primal problem and ...

IEEE2014

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

Model-based Sketching and Recovery with Expanders

Volkan Cevher, Luca Baldassarre, Bubacarr Bah

Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper conside ...

2014

A convex optimization framework for global tractography

Jean-Philippe Thiran, Alessandro Daducci, Alia Lemkaddem

In this article we present a novel approach for diffusion MRI global tractography. Our formulation models the signal in each voxel as a linear combination of fiber-tract basis func- tions, which consist of a comprehensive set of plausible fiber tracts that ...

2013

LAYOUT ASSESSMENT METHOD AND SYSTEM

Frank Grégoire Jean de Morsier, Virginia Estellers Casas, Nathalie Casati, Maria Gabrani

Candidate layout patterns can be assessed using a sparse pattern dictionary of known design layout patterns by determining sparse coefficients for each candidate pattern, reconstructing the respective candidate pattern, and determining reconstruction error ...

U.S. Patent and Trademark Office; U.S. DEPARTMENT OF COMMERCE2013

Optimal Sampling Rates in Infinite-Dimensional Compressed Sensing

Martin Vetterli, Loïc Arnaud Baboulaz, Mitra Fatemi

The theory of compressed sensing studies the problem of recovering a high dimensional sparse vector from its projections onto lower dimensional subspaces. The recently introduced framework of infinite-dimensional compressed sensing [1], to some extent gene ...

2013

Fast and robust estimation of jointly sparse channels

Martin Vetterli, Ali Hormati, Yann Barbotin

A device and method for estimating multipath jointly sparse channels. The method comprises receiving a number K of signal components by a number P of receiving antennas, where P≧2. The method further comprises estimating the sparsity condition of the multi ...

2013

A geometric framework for registration of sparse images

Pascal Frossard, Alhussein Fawzi

We examine the problem of image registration when images have a sparse representation in a dictionary of geometric features. We propose a novel algorithm for aligning images by pairing their sparse components. We show numerically that this algorithm works ...

2013