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Open AccessJournal ArticleDOI

Multiresolution approximations and wavelet orthonormal bases of L^2(R)

Stephane G. Mallat
- 01 Jan 1989 - 
- Vol. 315, Iss: 1, pp 69-87
TLDR
In this paper, the authors study the properties of multiresolution approximation and prove that it is characterized by a 2π periodic function, which is further described in terms of wavelet orthonormal bases.
Abstract
A multiresolution approximation is a sequence of embedded vector spaces   V j  jmember Z for approximating L 2 (R) functions. We study the properties of a multiresolution approximation and prove that it is characterized by a 2π periodic function which is further described. From any multiresolution approximation, we can derive a function ψ(x) called a wavelet such that   √  2 j ψ(2 j x −k)   (k ,j)member Z 2 is an orthonormal basis of L 2 (R). This provides a new approach for understanding and computing wavelet orthonormal bases. Finally, we characterize the asymptotic decay rate of multiresolution approximation errors for functions in a Sobolev space H s .

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Citations
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Book ChapterDOI

Families of wavelet transforms in connection with Shannon's sampling theory and the Gabor transform

TL;DR: In this article, the algebraic structure of nonorthogonal scaling functions, the multiresolutions they generate, and the wavelets associated with them are studied, and a framework for generating generalized sampling theories is proposed.
Journal ArticleDOI

Pointwise Convergence of Wavelet Expansions

TL;DR: In this article, the expansion of a distribution or function in regular orthogonal wavelets is considered and the expansion is shown to converge uniformly on compact subsets of intervals of continuity.
Proceedings ArticleDOI

Keyblock: an approach for content-based image retrieval

TL;DR: By comparing the performance of the proposed framework, Keyblock for content-based image retrieval, with the existing techniques using color feature and wavelet texture feature, the experimental results demonstrate the effectiveness of the framework in image retrieval.
Book ChapterDOI

Discrete Wavelet Transform

TL;DR: In this article, the authors proposed to reduce redundancy in the wavelet coefficients among different scales as much as possible, while at the same time, avoiding sacrificing the information contained in the original signal.

Investigation of Signal Characteristics Using the Continuous Wavelet Transform

John Sadowsky
TL;DR: The CWT is considered as a qualitative tool that can be used to analyze wideband communications signals and for finding features that might be of phenomenological significance in a seismic signal.
References
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Journal ArticleDOI

A theory for multiresolution signal decomposition: the wavelet representation

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI

Orthonormal bases of compactly supported wavelets

TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI

Decomposition of Hardy functions into square integrable wavelets of constant shape

TL;DR: In this article, the authors studied square integrable coefficients of an irreducible representation of the non-unimodular $ax + b$-group and obtained explicit expressions in the case of a particular analyzing family that plays a role analogous to coherent states (Gabor wavelets) in the usual $L_2 $ -theory.
Journal ArticleDOI

Exact reconstruction techniques for tree-structured subband coders

TL;DR: It is shown that it is possible to design tree-structured analysis/reconstruction systems which meet the sampling rate condition and which result in exact reconstruction of the input signal.
Journal ArticleDOI

Analysis of sound patterns through wavelet transforms

TL;DR: The main features of so-called wavelet transforms are illustrated through simple mathematical examples and the first applications of the method to the recognition and visualisation of characteristic features of speech and of musical sounds are presented.