Local Smoothing Methods in Image Processing

  • In this article a new data-adaptive method for smoothing of bivariate functions is developed. The smoothing is done by kernel regression with rotational invariant bivariate kernels. Two or three local bandwidth parameters are chosen automatically by a two-step plug-in approach. The algorithm starts with small global bandwidth parameters, which adapt during a few iterations to the noisy image. In the next step local bandwidths are estimated. Some general asymptotic results about Gasser-Müller-estimators and optimal bandwidth selection are given. The derived local bandwidth estimators converge and are asymptotically normal.

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Metadaten
Author:Vera Friederichs
URN (permanent link):urn:nbn:de:hbz:386-kluedo-14990
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (110)
Document Type:Preprint
Language of publication:English
Year of Completion:2007
Year of Publication:2007
Publishing Institute:Technische Universität Kaiserslautern
Tag:2-d kernel regression; data-adaptive bandwidth choice; iterative bandwidth choice; local bandwidths
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik

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