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Sparse Sampling: Theory, Algorithms and Applications

Objectif

Signal representations with Fourier and wavelet bases are central to signal processing and communications. Non-linear approximation methods in such bases are key for problems like denoising, compression and inverse problems. Recently, the idea that signals that are sparse in some domain can be acquired at low sampling density has generated strong interest, under various names like compressed sensing, compressive sampling and sparse sampling. We aim to study the central problem of acquiring continuous-time signals for discrete-time processing and reconstruction using the methods of sparse sampling. Solving this involves developing theory and algorithms for sparse sampling, both in continuous and discrete time. In addition, in order to acquire physical signals, we plan to develop a sampling theory for signals obeying physical laws, like the wave and diffusion equation, and light fields. Together, this will lead to a sparse sampling theory and framework for signal processing and communications, with applications from analog-to-digital conversion to new compression methods, to super-resolution data acquisition and to inverse problems in imaging. In sum, we aim to develop the theory and algorithms for sparse signal processing, with impact on a broad range of applications.

Appel à propositions

ERC-2009-AdG
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Régime de financement

ERC-AG - ERC Advanced Grant

Institution d’accueil

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Contribution de l’UE
€ 1 839 174,00
Adresse
BATIMENT CE 3316 STATION 1
1015 Lausanne
Suisse

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Région
Schweiz/Suisse/Svizzera Région lémanique Vaud
Type d’activité
Higher or Secondary Education Establishments
Chercheur principal
Martin Vetterli (Prof.)
Contact administratif
Caroline Vandevyver (Ms.)
Liens
Coût total
Aucune donnée

Bénéficiaires (1)