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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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TL;DR: In this paper, the authors identify multiresolution subspaces giving rise via Hankel transforms to Bessel functions, which emerge as orthogonal systems derived from geometric Hilbert-space considerations.
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An introduction to wavelet theory and application for the radiological physicist.

TL;DR: A primer on wavelet theory geared specifically for the radiological physicist is presented, free of the details of mathematical rigor, which are found in most other treatments of the subject and which are of little interest to physicists, yet is sufficient to convey a reasonably deep working knowledge ofWavelet theory.
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Multiresolution analysis of density fluctuation in supersonic mixing layer

TL;DR: In this paper, the multiresolution analysis of supersonic mixing layer based on experimental images is still a formidable challenge, due to the difficulties in measuring su-personic density field.
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Generalized B-spline signal processing

TL;DR: It is shown that the entire filtering process is equivalent to lowpass filtering of the band-limited signal and the antialiasing property of generalized B-spline filtering for band- limited signals is shown.
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.