Multivariate periodic wavelet analysis

Dirk Langemann*, Jürgen Prestin

*Korrespondierende/r Autor/-in für diese Arbeit
6 Zitate (Scopus)

Abstract

General multivariate periodic wavelets are an efficient tool for the approximation of multidimensional functions, which feature dominant directions of the periodicity. One-dimensional shift invariant spaces and tensor-product wavelets are generalized to multivariate shift invariant spaces on non-tensor-product patterns. In particular, the algebraic properties of the automorphism group are investigated. Possible patterns are classified. By divisibility considerations, decompositions of shift invariant spaces are given. The results are applied to construct multivariate orthogonal Dirichlet kernels and the respective wavelets. Furthermore a closure theorem is proven.

OriginalspracheEnglisch
ZeitschriftApplied and Computational Harmonic Analysis
Jahrgang28
Ausgabenummer1
Seiten (von - bis)46-66
Seitenumfang21
ISSN1063-5203
DOIs
PublikationsstatusVeröffentlicht - 01.01.2010

Fingerprint

Untersuchen Sie die Forschungsthemen von „Multivariate periodic wavelet analysis“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren