SPARCS

Statistical Physics Approach to Reconstruction in Compressed Sensing

Coordinatore	CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	France [FR]
Totale costo	1˙077˙960 €
EC contributo	1˙077˙960 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2012-StG_20111012
Funding Scheme	ERC-SG
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-10-01   -   2017-09-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE  Organization address address: Rue Michel -Ange 3 city: PARIS postcode: 75794 contact info Titolo: Dr. Nome: Florent Cognome: Krzakala Email: send email Telefono: +33 1 44323419 Fax: +33 1 44323433	FR (PARIS)	hostInstitution	1˙077˙960.00
2	CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE  Organization address address: Rue Michel -Ange 3 city: PARIS postcode: 75794 contact info Titolo: Mr. Nome: Louis Cognome: Avigdor Email: send email Telefono: 33142349417 Fax: 33142349508	FR (PARIS)	hostInstitution	1˙077˙960.00

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 Obiettivo del progetto (Objective)

'Compressed sensing is triggering a major evolution in signal acquisition: it indicates that most data, signals and images, that are usually compressible and have redundancy, can be reconstructed from much fewer measurements than what was usually considered necessary, resulting in a drastic gain of time, cost, and measurement precision. In order to make this groundbreaking improvement possible, compressed sensing deals with how measurements should be performed, and how, in a second step, to use computational power in order to reconstruct the original signal. Compressed sensing can be used for many applications (speeding up magnetic resonance imaging without the loss of resolution, performing X-ray scans with less radiation exposure, sensing and compressing data simultaneously, measurements in acoustic holography, in system biology, faster confocal microscopy, etc ...). Currently used measurement protocols and reconstruction techniques, however, are still limited to acquisition rates considerably higher than what is theoretically necessary.

The aim of this project is to develop a new interdisciplinary approach to compressed sensing, based on a statistical physics inspired methodology, whose preliminary application by the PI already yield spectacular results. I propose to use both a new algorithm for the reconstruction algorithm, with a mean-field inspired “Belief Propagation” method, and a new class of compressed sensing measurement schemes, motivated by a statistical physics study of the problem and by the theory of crystal nucleation in first order transitions. For reasons detailed below, this statistical physics approach is extremely promising theoretical framework to tackle compressed sensing and I believe it can eventually lead to optimal performance. I expect that the progress we will make in this direction will be instrumental also for other inference and inverse problems at the crossroad between physics and computer science.'