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UID:8119@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20150327T140000
DTEND;TZID=Europe/Paris:20150327T150000
DTSTAMP:20241120T210035Z
URL:https://www.i2m.univ-amu.fr/evenements/s-kitic-inria-sparsity-co-regul
 arization-of-the-physics-driven-and-audio-inverse-problems/
SUMMARY: (...): S. Kitic (Inria): Sparsity & Co.: Regularization of the phy
 sics-driven and audio inverse problems
DESCRIPTION:: Sparsity & Co.: Regularization of the physics-driven and audi
 o inverse problems\nBy Srđan Kitić\\\, Inria.\n\nLow dimensional data mo
 dels are powerful tools for regularizing ill-posed inverse problems. In th
 is work\\\, we investigate the means and performance of the sparse and cos
 parse (aka sparse analysis) data models for physics-driven and audio inver
 se problems.\n\nPhysics-driven inverse problems naturally arise from physi
 cal laws expressed through linear partial differential equations. We intro
 duce a regularization framework based on the two data models\\\, and show 
 that\\\, despite nominal equivalence\\\, the two models significantly diff
 er from the computational perspective. Our findings are illustrated on two
  example applications: sound and brain source localization (electroencepha
 lography - EEG). \n\nIn the second part of the talk\\\, we explore the ill
 -posed problem of de-saturation (de-clipping) of audio signals. We compare
  the performance of the two data models embedded in greedy heuristics\\\, 
 and show that they outperform state-of-the-art methods in the field.\n
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