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Proceedings Paper

Design-adapted wavelet estimator for two-dimensional tensor product irregular designs
Author(s): Véronique A Delouille; Jo Simoens; Rainer von Sachs
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Paper Abstract

We treat nonparametric estimation of a regression function defined on a 'tensor product irregular grid,' that is, a grid constructed as the Cartesian product of two irregular one-dimensional grids. Our wavelet-type estimator is based on a wavelet transform which is the tensor product of two one-dimensional design-adapted wavelet transforms. We propose a denoising scheme and show the performance of the resulting estimator through a simulation study.

Paper Details

Date Published: 13 November 2003
PDF: 12 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.505662
Show Author Affiliations
Véronique A Delouille, Rice Univ. (United States)
Jo Simoens, Katholieke Univ. Leuven (Belgium)
Rainer von Sachs, Univ. Catholique de Louvain (Belgium)


Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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