A Banach space of test functions for Gabor analysis

  • Hans G. Feichtinger
  • Georg Zimmermann
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Abstract

We introduce the Banach space S 0L 2 which has a variety of properties making it a useful tool in Gabor analysis. S 0 can be characterized as the smallest time-frequency homogeneous Banach space of (continuous) functions. We also present other characterizations of S 0 turning it into a very flexible tool for Gabor analysis and allowing for simplifications of various proofs. A careful analysis of both the coefficient and the synthesis mapping in Gabor theory shows that an arbitrary window in S 0 not only is a Bessel atom with respect to arbitrary time-frequency lattices, but also yields boundedness between S 0 and e 1. On the other hand, we can study properties of general L 2-atoms since they induce mappings from S 0 to S0. This enables us to introduce a new, very natural concept of weak duality of Gabor atoms, applying also to the classical pair of the Gauss-function and its dual function determined by Bastiaans. Using the established results, we show a variety of properties that are desirable in applications, like the continuous dependence of the canonical dual window on the given Gabor window and on the lattice; continuity of thresholding and masking operators from signal processing; and an algorithm for the reconstruction of bandlimited functions from samples of the Gabor transform in a corresponding horizontal strip in the time-frequency plane. We also present an approximate Balian-Low Theorem stating that for close-to-critical lattices, the dual Gabor atoms progressively lose their time-frequency localization.

Keywords

Convolution Radon Verse 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Hans G. Feichtinger
  • Georg Zimmermann

There are no affiliations available

Personalised recommendations