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Probability Theory and Related Fields

, Volume 121, Issue 2, pp 137–170 | Cite as

Nonlinear estimation in anisotropic multi-index denoising

  • Gérard Kerkyacharian
  • Oleg Lepski
  • Dominique Picard

Abstract.

In the framework of denoising a function depending of a multidimensional variable (for instance an image), we provide a nonparametric procedure which constructs a pointwise kernel estimation with a local selection of the multidimensional bandwidth parameter. Our method is a generalization of the Lepski's method of adaptation, and roughly consists in choosing the “coarsest” bandwidth such that the estimated bias is negligible. However, this notion becomes more delicate in a multidimensional setting. We will particularly focus on functions with inhomogeneous smoothness properties and especially providing a possible disparity of the inhomogeneous aspect in the different directions. We show, in particular that our method is able to exactly attain the minimax rate or to adapt to unknown degree of anisotropic smoothness up to a logarithmic factor, for a large scale of anisotropic Besov spaces.

Keywords

Besov Space Kernel Estimation Nonlinear Estimation Local Selection Logarithmic Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Gérard Kerkyacharian
    • 1
  • Oleg Lepski
    • 2
  • Dominique Picard
    • 3
  1. 1.Laboratoire de Probabilités et Modèles Aléatoires, CNRS-UMR 7599; et Université Paris X, 200, avenue de la République, F-92001 Nanterre, FranceFR
  2. 2.Laboratoire d'Analyse, Probabilitiés, Topologie, CNRS-UMR 6632, Université de Provence, 39 rue F. Joliot-Curie, 13453 Marseille Cedex 13, France. e-mail: Oleg.Lepski@gyptis.univ-mrs.frFR
  3. 3.Laboratoire de Probabilités et Modèles Aléatoires, CNRS-UMR 7599; et Université Paris VII, 16, rue de Clisson, F-75013 Paris, FranceFR

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