# Multiresolution analysis

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 L. Crowley.

## Definition[edit]

A multiresolution analysis of the Lebesgue space consists of a sequence of nested subspaces

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*is invariant under shifts by integer multiples of_{k}*2*. That is, for each the function^{k}*g*defined as also contained in .*Self-similarity*in*scale*demands that all subspaces are time-scaled versions of each other, with scaling respectively dilation factor 2^{k-l}. I.e., for each there is a with .- In the sequence of subspaces, for
*k*>*l*the space resolution 2^{l}of the*l*-th subspace is higher than the resolution 2^{k}of the*k*-th subspace. *Regularity*demands that the model subspace*V*be generated as the linear hull (algebraically or even topologically closed) of the integer shifts of one or a finite number of generating functions or . Those integer shifts should at least form a frame for the subspace , which imposes certain conditions on the decay at infinity. The generating functions are also known as_{0}**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 in , and that they are not too redundant, i.e., their intersection should only contain the zero element.

## Important conclusions[edit]

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 implies that there is a finite sequence of coefficients for , and for , such that

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

one can show that the space , which is defined as the (closed) linear hull of the mother wavelet's integer shifts, is the orthogonal complement to inside .^{[1]} Or put differently, is the orthogonal sum (denoted by ) of and . By self-similarity, there are scaled versions of and by completeness one has

thus the set

is a countable complete orthonormal wavelet basis in .

## See also[edit]

## References[edit]

**^**Mallat, S.G. "A Wavelet Tour of Signal Processing".*www.di.ens.fr*. Retrieved 2019-12-30.

- Chui, Charles K. (1992).
*An Introduction to Wavelets*. San Diego: Academic Press. ISBN 0-585-47090-1. - Akansu, A.N.; Haddad, R.A. (1992).
*Multiresolution signal decomposition: transforms, subbands, and wavelets*. Academic Press. ISBN 978-0-12-047141-6. - Crowley, J. L., (1982). A Representations for Visual Information, Doctoral Thesis, Carnegie-Mellon University, 1982.
- Burrus, C.S.; Gopinath, R.A.; Guo, H. (1997).
*Introduction to Wavelets and Wavelet Transforms: A Primer*. Prentice-Hall. ISBN 0-13-489600-9. - Mallat, S.G. (1999).
*A Wavelet Tour of Signal Processing*. Academic Press. ISBN 0-12-466606-X.