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UID:3648@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20150327T140000
DTEND;TZID=Europe/Paris:20150327T150000
DTSTAMP:20150312T130000Z
URL:https://www.i2m.univ-amu.fr/events/s-kitic-inria-sparsity-co-regulariz
ation-of-the-physics-driven-and-audio-inverse-problems/
SUMMARY:S. Kitic (Inria): Sparsity & Co.: Regularization of the physics-dri
ven and audio inverse problems -
DESCRIPTION:Sparsity & Co.: Regularization of the physics-driven and audio
inverse problems\nBy Srđan Kitić\\\, Inria.\n\nLow dimensional data mode
ls are powerful tools for regularizing ill-posed inverse problems. In this
work\\\, we investigate the means and performance of the sparse and cospa
rse (aka sparse analysis) data models for physics-driven and audio inverse
problems.\n\nPhysics-driven inverse problems naturally arise from physica
l laws expressed through linear partial differential equations. We introdu
ce a regularization framework based on the two data models\\\, and show th
at\\\, despite nominal equivalence\\\, the two models significantly differ
from the computational perspective. Our findings are illustrated on two e
xample applications: sound and brain source localization (electroencephalo
graphy - EEG). \n\nIn the second part of the talk\\\, we explore the ill-p
osed problem of de-saturation (de-clipping) of audio signals. We compare t
he performance of the two data models embedded in greedy heuristics\\\, an
d show that they outperform state-of-the-art methods in the field.\n
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DTSTART:20141026T020000
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