Towards a Robust Imprecise Linear Deconvolution

Olivier Strauss 1 Agnès Rico 2
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Deconvolution consists of reconstructing a signal from blurred (and usually noisy) sensory observations. It requires perfect knowledge of the impulse response of the sensor. Relevant works in the litterature propose methods with improved precision and robustness. But those methods are not able to account for a partial knowledge of the impulse response of the sensor. In this article, we experimentally show that inverting a Choquet capacity-based model of an imprecise knowledge of this impulse response allows to robustly recover the measured signal. The method we use is an interval valued extension of the well known Schultz procedure.
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Chapitre d'ouvrage
Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer, pp.55-62, 2013, Advances in Intelligent Systems and Computing, 〈10.1007/978-3-642-33042-1_7〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00862679
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Soumis le : mardi 17 septembre 2013 - 11:52:13
Dernière modification le : mardi 16 octobre 2018 - 07:52:01

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Olivier Strauss, Agnès Rico. Towards a Robust Imprecise Linear Deconvolution. Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer, pp.55-62, 2013, Advances in Intelligent Systems and Computing, 〈10.1007/978-3-642-33042-1_7〉. 〈lirmm-00862679〉

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