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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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Journal ArticleDOI

Development of wavelet transform voltammetric analyzer

TL;DR: An on-line wavelet transform algorithm and development of voltammetric analyzer with the on-linesWavelet transform (WT-voltammetrics analyzer) are described, which shows that the overlapping peaks of Pb(II) and Tl(I) can be separated easily, and the peak position after the on theline wave let transform does not change.
Proceedings ArticleDOI

Wavelet transform-based texture classification with feature weighting

TL;DR: A new approach to wavelet transform-based texture classification using feature weighting, which extracts the l/sub 1/-norm of the wavelet Transform output as the features for texture classification, and weights these features according to their own degree of dispersion.
Proceedings ArticleDOI

Parallel Algorithms for the Two-Dimensional Discrete Wavelet Transform

TL;DR: A mathematical model for the computation vs. communication tradeoff for these algorithms is presented and the scalability of the algorithms is analyzed.
Journal ArticleDOI

Estimation of the integrated squared density derivatives by wavelets

TL;DR: In this paper, the problem of estimating the integral of the squared derivative of a probability density f is considered using wavelet orthonormal bases, and the precise asymptotic expression for the mean integrated squared error of the wavelet estimator is derived.
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.