LANTÉRI , Henri

This book deals with functions allowing to express the dissimilarity (discrepancy) between two data fields or "divergence functions" with the aim of applications to linear inverse problems.\\Most of the divergences found in the litterature are used in the field of information theory to quantify the difference between two probability density...

Logarithmes déformés.-Divergences entropiques associées.-Applications aux problèmes inverses linéaires.-Algorithmes d'inversion.

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This paper deals with the linear unmixing problem in hyperspectral data processing, and in particular the estimation of the fractional abundances under sum-to-one and non-negativity constraints. For this purpose, we propose to adapt the reflect-then-combine iterative technique, initially derived by Cimmino. Several strategies are studied in...

The estimation of fractional abundances under physical constraints is a fundamental problem in hyperspectral data processing. In this paper, we propose to adapt Kaczmarz's cyclic projections to solve this problem. The main contribution of this work is two-fold: On the one hand, we show that the non-negativity and the sum-to-one constraints can...

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-Le travail présenté concerne la déconvolution d'images par minimisation de divergences invariantes par changement d'échelle. L'intérêt de telles divergences est d'inclure par elles-mêmes une contrainte de somme invariante sur la reconstruction. En utilisant ce type de divergence à la fois pour le terme d'attache aux données et pour le terme de...

-Nous décrivons l'inversion de l'intégrale de Fredholm pour des images subissant un fort vignetage conduisant à des PSFs dites évanescentes car disparaissant vers un bord du champ. L'algorithme utilisé, de type gradient avec contrainte de non-négativité, utilise une régularisation adaptative et automatique faisant usage du rapport de Strehl....

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This paper addresses the problem of linear unmixing for hyperspectral imagery. This problem can be formulated as a linear regression problem whose regression coefficients (abundances) satisfy sum-to-one and positivity constraints. Two scaled gradient iterative methods are proposed for estimating the abundances of the linear mixing model. The...

We develop a self-consistent automatic procedure to restore informations from astronomical observations. It relies on both a new deconvolution algorithm called LBCA (Lower Bound Constraint Algorithm) and the use of the Wiener filter. In order to explore its scientific potential for strong and weak gravitational lensing, we process a CFHT image...

Dynamic system modeling plays a crucial role in the development of techniques for stationary and non-stationary signal processing. Due to the inherent physical characteristics of systems usually under investigation, non-negativity is a desired constraint that can be imposed on the parameters to estimate. In this paper, we propose a general...

Information processing with L1-norm constraint has been a topic of considerable interest during the last five years since it produces sparse solutions. Non-negativity constraints are also desired properties that can usually be imposed due to inherent physical characteristics of real-life phenomena. In this paper, we investigate an online method...

Linear unmixing of hyperspectral images is a popular approach to determine and quantify materials in sensed images. The linear unmixing problem is challenging because the abundances of materials to estimate have to satisfy non-negativity and full-additivity constraints. In this paper, we investigate an iterative algorithm that integrates these...

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Cet article décrit une nouvelle classe d'algorithmes d'apprentissage non-linéaires en ligne avec contrainte de positivité sur la solution. Ceux-ci sont appliqués au problème d'identification distribuée d'un champ scalaire positif, par exemple de rayonnement thermique ou de concentration d'une espèce chimique, par un réseau de capteurs. La...

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