The mathematical theory of wavelets (original) (raw)

2001, … analysis—a celebration (Il Ciocco, 2000)

We present an overview of some aspects of the mathematical theory of wavelets. These notes are addressed to an audience of mathematicians familiar with only the most basic elements of Fourier Analysis. The material discussed is quite broad and covers several topics involving wavelets. Though most of the larger and more involved proofs are not included, complete references to them are provided. We do, however, present complete proofs for results that are new (in particular, this applies to a recently obtained characterization of "all" wavelets in section 4).

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Introduction to Wavelets

1993

The past ten years have seen an explosion of research in the theory of wavelets and their applications. Theoretical accomplishments include development of new bases for many different function spaces and the characterization of orthonormal wavelets with compact support. Applications span the fields of signal processing, image processing and compression, data compression, and quantum mechanics. At the present time however, much of the literature remains highly mathematical, and consequently, a large investment of time is often necessary to develop a general understanding of wavelets and their potential uses. This paper thus seeks to provide an overview of the wavelet transform from an intuitive standpoint. Throughout the paper a signal processing frame of reference is adopted.

Unitary matrix functions, wavelet algorithms, and structural properties of wavelets

2004

Some connections between operator theory and wavelet analysis: Since the mid eighties, it has become clear that key tools in wavelet analysis rely crucially on operator theory. While isolated variations of wavelets, and wavelet constructions had previously been known, since Haar in 1910, it was the advent of multiresolutions, and sub-band filtering techniques which provided the tools for our ability to now easily create efficient algorithms, ready for a rich variety of applications to practical tasks. Part of the underpinning for this development in wavelet analysis is operator theory. This will be presented in the lectures, and we will also point to a number of developments in operator theory which in turn derive from wavelet problems, but which are of independent interest in mathematics. Some of the material will build on chapters in a new wavelet book, co-authored by the speaker and Ola Bratteli, see http://www.math.uiowa.edu/˜jorgen/. Contents Abstract 1 1. Introduction 3 1.1. Index of terminology in math and in engineering 4 1.1.1. Some background on Hilbert space 1.1.2. Connections to group theory 1.1.3. Some background on matrix functions in mathematics and in engineering 1.

A Note on Some New Generalized Wavelets

Journal of Mathematics

In this paper, we define new real wavelets based on the Hartley kernel and Boas transforms. These wavelets have possible application in analyzing both the symmetries of an asymmetric real signal. We give various results to obtain their higher vanishing moments. Finally, we give a sufficient condition under which Hartley-Boas-Like wavelets associated with Riesz projector forms a convolution filter with transfer function vanishing for the positive frequencies.

On the Hilbert Transform of Wavelets

IEEE Transactions on Signal Processing, 2011

A wavelet is a localized function having a prescribed number of vanishing moments. In this correspondence, we provide precise arguments as to why the Hilbert transform of a wavelet is again a wavelet. In particular, we provide sharp estimates of the localization, vanishing moments, and smoothness of the transformed wavelet. We work in the general setting of non-compactly supported wavelets. Our main result is that, in the presence of some minimal smoothness and decay, the Hilbert transform of a wavelet is again as smooth and oscillating as the original wavelet, whereas its localization is controlled by the number of vanishing moments of the original wavelet.

Construction of Wavelets and Applications

A sequence of increasing translation invariant subspaces can be defined by the Haar-system (or generally by wavelets). The orthogonal projection to the subspaces generates a decomposition (multiresolution) of a signal. Regarding the rate of convergence and the number of operations, this kind of decomposition is much more favorable then the conventional Fourier expansion.

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