FUN-SP

"A functional framework for sparse, non-gaussian signal processing and bioimaging"

Coordinatore	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Switzerland [CH]
Totale costo	2˙106˙994 €
EC contributo	2˙106˙994 €
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-2010-AdG_20100224
Funding Scheme	ERC-AG
Anno di inizio	2011
Periodo (anno-mese-giorno)	2011-04-01   -   2016-03-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE  Organization address address: BATIMENT CE 3316 STATION 1 city: LAUSANNE postcode: 1015 contact info Titolo: Ms. Nome: Caroline Cognome: Vandevyver Email: send email Telefono: +41 21 693 4977 Fax: +41 21 693 5585	CH (LAUSANNE)	hostInstitution	2˙106˙994.00
2	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE  Organization address address: BATIMENT CE 3316 STATION 1 city: LAUSANNE postcode: 1015 contact info Titolo: Prof. Nome: Michael Cognome: Unser Email: send email Telefono: +41 21 693 51 75	CH (LAUSANNE)	hostInstitution	2˙106˙994.00

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 Obiettivo del progetto (Objective)

'In recent years, the research focus in signal processing has shifted away from the classical linear paradigm, which is intimately linked with the theory of stationary Gaussian processes. Instead of considering Fourier transforms and performing quadratic optimization, researchers are presently favoring wavelet-like representations and have adopted ”sparsity” as design paradigm.

Our ambition is to develop a unifying operator-based framework for signal processing that would provide the ``sparse' counterpart of the classical theory, which is currently missing. To that end, we shall specify and investigate sparse stochastic processes that are continuously-defined and ruled by differential equations, and construct corresponding wavelet-like sparsifying transforms. Our hope is to be able to rigorously connect non-quadratic regularization and sparsity-constrained optimization to well-defined continuous-domain statistical models. We also want to develop a novel Lie-group formalism for the design of steerable, signal-adapted wavelet transforms with improved invariance and sparsifying properties, both in 2-D and 3-D.

We shall use these tools to define new reversible image representations in terms of singular points (contours and keypoints) and to develop novel algorithms for 3-D biomedical image analysis. In close collaboration with imaging scientists, we shall apply our framework to the development of new 3-D reconstruction algorithms for emerging bioimaging modalities such as fluorescence deconvolution microscopy, digital holography microscopy, X-ray phase-contrast microscopy, and advanced MRI.'