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Resolution of the wavefront set using general continuous wavelet transforms


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Fell, Jonathan ; Führ, Hartmut ; Voigtlaender, Felix:
Resolution of the wavefront set using general continuous wavelet transforms.
In: The journal of Fourier analysis and applications. 22 (2016) 5. - S. 997-1058.
ISSN 1069-5869 ; 1531-5851


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We consider the problem of characterizing the wavefront set of a tempered distribution u∈S′(Rd) in terms of its continuous wavelet transform, where the latter is defined with respect to a suitably chosen dilation group H⊂GL(Rd). In this paper we develop a comprehensive and unified approach that allows to establish characterizations of the wavefront set in terms of rapid coefficient decay, for a large variety of dilation groups.
For this purpose, we introduce two technical conditions on the dual action of the group H, called microlocal admissibilty and (weak) cone approximation property. Essentially, microlocal admissibilty sets up a systematical relationship between the scales in a wavelet dilated by h∈H on one side, and the matrix norm of h on the other side. The (weak) cone approximation property describes the ability of the wavelet system to adapt its frequency-side localization to arbitrary frequency cones. Together, microlocal admissibility and the weak cone approximation property allow the characterization of points in the wavefront set using multiple wavelets. Replacing the weak cone approximation by its stronger counterpart gives access to single wavelet characterizations.
We illustrate the scope of our results by discussing -- in any dimension d≥2 -- the similitude, diagonal and shearlet dilation groups, for which we verify the pertinent conditions. As a result, similitude and diagonal groups can be employed for multiple wavelet characterizations, whereas for the shearlet groups a single wavelet suffices. In particular, the shearlet characterization (previously only established for d=2) holds in arbitrary dimensions.

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Sprache des Eintrags:Englisch
Institutionen der Universität:Mathematisch-Geographische Fakultät > Mathematik > Lehrstuhl für Mathematik - Reliable Machine Learning
Mathematisch-Geographische Fakultät > Mathematik > Mathematisches Institut für Maschinelles Lernen und Data Science (MIDS)
DOI / URN / ID:10.1007/s00041-015-9445-7
Verlag:Birkhäuser Boston
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Titel an der KU entstanden:Nein
Eingestellt am: 01. Apr 2022 14:34
Letzte Änderung: 01. Jun 2023 15:40
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