Szemerédi regularity lemma: Difference between revisions

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A '''multiresolution analysis (MRA)''' or '''multiscale approximation (MSA)''' is the design method of most of the practically relevant [[discrete wavelet transform]]s (DWT) and the justification for the [[algorithm]] of the [[fast wavelet transform]] (FWT). It was introduced in this context in 1988/89 by [[Stephane Mallat]] and [[Yves Meyer]] and has predecessors in the [[microlocal analysis]] in the theory of [[differential equation]]s (the ''[[ironing method]]'') and the [[pyramid (image processing)|pyramid method]]s of [[image processing]] as introduced in 1981/83 by Peter J. Burt, Edward H. Adelson and James Crowley.
 
== Definition ==
 
A ''multiresolution analysis'' of the [[Lp space|space]] <math>L^2(\mathbb{R})</math> consists of a [[sequence]] of nested [[linear subspace|subspaces]]
 
::<math>\{0\}\dots\subset V_0\subset V_1\subset\dots\subset V_n\subset V_{n+1}\subset\dots\subset L^2(\R)</math>
 
that satisfies certain self-similarity relations in time/space and scale/frequency, as well as [[completeness]] and regularity relations.
 
* ''Self-similarity'' in ''time'' demands that each subspace ''V<sub>k</sub>'' is invariant under shifts by [[integer]] [[multiple (mathematics)|multiple]]s of ''2<sup>-k</sup>''. That is, for each <math>f\in V_k,\; m\in\mathbb Z</math> there is a <math>g\in V_k</math> with <math>\forall x\in\mathbb R:\;f(x)=g(x+m2^{-k})</math>.
* ''Self-similarity'' in ''scale'' demands that all subspaces <math>V_k\subset V_l,\; k<l,</math> are time-scaled versions of each other, with [[Scaling_(geometry)|scaling]] respectively [[Dilation (metric space)|dilation]] factor ''2<sup>l-k</sup>''. I.e., for each <math>f\in V_k</math> there is a <math>g\in V_l</math> with <math>\forall x\in\mathbb R:\;g(x)=f(2^{l-k}x)</math>. If f has limited [[support (mathematics)|support]], then as the support of g gets smaller, the resolution of the ''l''-th subspace is higher than the resolution of the ''k''-th subspace.
 
* ''Regularity'' demands that the model subspace ''V<sub>0</sub>'' be generated as the [[linear hull]] ([[algebraic closure|algebraically]] or even [[topologically closed]]) of the integer shifts of one or a finite number of generating functions <math>\phi</math> or <math>\phi_1,\dots,\phi_r</math>. Those integer shifts should at least form a frame for the subspace <math>V_0\subset L^2(\R)</math>, which imposes certain conditions on the decay at infinity. The generating functions are also known as '''[[Wavelet#Scaling_function|scaling functions]]''' or '''[[father wavelets]]'''. In most cases one demands of those functions to be [[piecewise continuous]] with [[compact support]].
 
* ''Completeness'' demands that those nested subspaces fill the whole space, i.e., their union should be [[dense set|dense]] in <math>L^2(\mathbb{R})</math>, and that they are not too redundant, i.e., their intersection should only contain the zero element.
 
== Important conclusions ==
In the case of one continuous (or at least with bounded variation) compactly supported scaling function with orthogonal shifts, one may make a number of deductions. The proof of existence of this class of functions is due to [[Ingrid Daubechies]].
 
Assuming the scaling function has compact support, then <math>V_0\subset V_1</math> implies that there is a finite sequence of coefficients <math>a_k=2 \langle\phi(x),\phi(2x-k)\rangle</math> for <math>|k|\leq N</math>, and  <math>a_k=0</math> for <math>|k|>N</math>, such that
:<math>\phi(x)=\sum_{k=-N}^N a_k\phi(2x-k).</math>
 
Defining another function, known as '''mother wavelet''' or just '''the wavelet'''
 
:<math>\psi(x):=\sum_{k=-N}^N (-1)^k a_{1-k}\phi(2x-k).</math>
 
One can show that the space <math>W_0\subset V_1</math>, which is defined as the (closed) linear hull of the mother wavelet's integer shifts, is the orthogonal complement to <math>V_0</math> inside <math>V_1</math>.{{cn|date=April 2013}} Or put differently, <math>V_1</math> is the [[orthogonal direct sum|orthogonal sum]] (denoted by <math>\oplus</math>) of <math>W_0</math> and <math>V_0</math>. By self-similarity, there are scaled versions <math>W_k</math> of <math>W_0</math> and by completeness one has{{cn|date=April 2013}}
 
:<math>L^2(\mathbb R)=\mbox{closure of }\bigoplus_{k\in\Z}W_k,</math>
 
thus the set
 
:<math>\{\psi_{k,n}(x)=\sqrt2^k\psi(2^kx-n):\;k,n\in\Z\}</math>
 
is a countable complete [[orthonormal wavelet]] basis in <math>L^2(\R)</math>.
 
==See also==
* [[Multiscale modeling]]
* [[Scale space]]
* [[Wavelet]]
 
{{inline|date=April 2013}}
 
==References==
* {{cite book|first=Charles K.|last=Chui|title=An Introduction to Wavelets|year=1992|publisher=Academic Press|location=San Diego|isbn=0-585-47090-1}}
 
* {{cite book|author1-link=Ali Akansu|first1=A.N.|last1=Akansu|first2=R.A.|last2=Haddad|title=Multiresolution signal decomposition: transforms, subbands, and wavelets|publisher=Academic Press|year=1992|isbn=978-0-12-047141-6}}
 
* {{cite book|author1-link=C. Sidney Burrus|first1=C.S.|last1=Burrus|first2=R.A.|last2=Gopinath|first3=H.|last3=Guo|title=Introduction to Wavelets and Wavelet Transforms: A Primer|publisher=Prentice-Hall|year=1997|isbn=0-13-489600-9}}
 
* {{cite book|first=S.G.|last=Mallat|url=http://www.cmap.polytechnique.fr/~mallat/book.html|title=A Wavelet Tour of Signal Processing|publisher=Academic Press|year=1999|isbn=0-12-466606-X}}
 
== External links ==
*[http://www.docstoc.com/docs/160022503/A-Concise-Introduction-to-Wavelets A Concise Introduction to Wavelets (with  coverage of multiresolution analysis)] by René Puchinger.
 
[[Category:Wavelets]]
[[Category:Time–frequency analysis]]

Revision as of 03:48, 2 February 2014

A multiresolution analysis (MRA) or multiscale approximation (MSA) is the design method of most of the practically relevant discrete wavelet transforms (DWT) and the justification for the algorithm of the fast wavelet transform (FWT). It was introduced in this context in 1988/89 by Stephane Mallat and Yves Meyer and has predecessors in the microlocal analysis in the theory of differential equations (the ironing method) and the pyramid methods of image processing as introduced in 1981/83 by Peter J. Burt, Edward H. Adelson and James Crowley.

Definition

A multiresolution analysis of the space L2() consists of a sequence of nested subspaces

{0}V0V1VnVn+1L2()

that satisfies certain self-similarity relations in time/space and scale/frequency, as well as completeness and regularity relations.

  • Completeness demands that those nested subspaces fill the whole space, i.e., their union should be dense in L2(), and that they are not too redundant, i.e., their intersection should only contain the zero element.

Important conclusions

In the case of one continuous (or at least with bounded variation) compactly supported scaling function with orthogonal shifts, one may make a number of deductions. The proof of existence of this class of functions is due to Ingrid Daubechies.

Assuming the scaling function has compact support, then V0V1 implies that there is a finite sequence of coefficients ak=2ϕ(x),ϕ(2xk) for |k|N, and ak=0 for |k|>N, such that

ϕ(x)=k=NNakϕ(2xk).

Defining another function, known as mother wavelet or just the wavelet

ψ(x):=k=NN(1)ka1kϕ(2xk).

One can show that the space W0V1, which is defined as the (closed) linear hull of the mother wavelet's integer shifts, is the orthogonal complement to V0 inside V1.Template:Cn Or put differently, V1 is the orthogonal sum (denoted by ) of W0 and V0. By self-similarity, there are scaled versions Wk of W0 and by completeness one hasTemplate:Cn

L2()=closure of kWk,

thus the set

{ψk,n(x)=2kψ(2kxn):k,n}

is a countable complete orthonormal wavelet basis in L2().

See also

Template:Inline

References

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