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

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.

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 implies that there is a finite sequence of coefficients for |k|\leq N, and a_k=0 for |k|>N, 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 V_0 inside V_{-1}. Or put differently, V_{-1} is the orthogonal sum (denoted by \oplus) of W_0 and V_0. By self-similarity, there are scaled versions W_k of W_0 and by completeness one has thus the set is a countable complete orthonormal wavelet basis in L^2(\R).