Wavelet
Encyclopedia : W : WA : WAV : Wavelet
In mathematics, wavelets, wavelet analysis, and the wavelet transform refers to the representation of a signal in terms of a finite length or fast decaying oscillating waveform (known as the mother wavelet). This waveform is scaled and translated to match the input signal. In formal terms, this representation is a wavelet series, which is the coordinate representation of a square integrable function with respect to a complete, orthonormal set of basis functions for the Hilbert space of square integrable functions. Note that the wavelets in the JPEG2000 standard are biorthogonal wavelets, that is, the coordinates in the wavelet series are computed with a different, dual set of basis functions.
Overview
The word wavelet is due to Morlet and Grossman in the early 1980s. They used the French word ondelette - meaning "small wave". A little later it was transformed into English by translating "onde" into "wave" - giving wavelet. Wavelet transforms are broadly classified into the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT). The principal difference between the two is the continuous transform operates over every possible scale and translation whereas the discrete uses a specific subset of all scale and translation values.Using wavelet theory
Wavelet theory is applicable to several other subjects. All wavelet transforms may be considered to be forms of time-frequency representation and are, therefore, related to the subject of harmonic analysis. Almost all practically useful discrete wavelet transforms make use of filterbanks containing finite impulse response filters. The wavelets forming a CWT are subject to Heisenberg's uncertainty principle and, equivalently, discrete wavelet bases may be considered in the context of other forms of the uncertainty principle.Mother wavelet
For practical applications one prefers for efficiency reasons continuously differentiable functions with compact support as mother (prototype) wavelet (functions). However, to satisfy analytical requirements (in the continuous WT) and in general for theoretical reasons one chooses the wavelet functions from a subspace of the space [L^1(\R)\cap L^2(\R)]. This is the space of measurable functions that are both absolutely and square integrable:- [\int_^ |\psi (t)|\, dt <\infty] and [\int_^ |\psi (t)|^2 \, dt <\infty].
- [\int_^ \psi (t)\, dt = 0] is the condition for zero mean, and
- [\int_^ |\psi (t)|^2\, dt = 1] is the condition for square norm one.
For the discrete wavelet transform, one needs at least the condition that the wavelet series is a representation of the identity in the space [L^2(\R)]. Most constructions of discrete WT make use of the multiresolution analysis, which defines the wavelet by a scaling function. This scaling function itself is solution to a functional equation.
In most situations it is useful to restrict [\psi] to be a continuous function with a higher number M of vanishing moments, i.e. for all integer m
The mother wavelet is scaled (or dilated) by a factor of [a] and translated (or shifted) by a factor of [b] to give (under Morlet's original formulation):
These functions are often incorrectly referred to as the basis functions of the (continuous) transform. In fact, as in the continuous Fourier transform, there is no basis in the continuous wavelet transform. Time-frequency interpretation uses a subtly different formulation (after Delprat).
The discrete wavelet transform is also less computationally complex, taking O(N) time as compared to O(N log N) for the fast Fourier transform (N is the data size).
For analysis the high pass filter is calculated as the QMF of the low pass, and reconstruction filters the time reverse of the decomposition.
Daubechies and Symlet wavelets can be defined by the scaling filter.
The wavelet function is in effect a band-pass filter and scaling it for each level halves its bandwidth. This creates the problem that in order to cover the entire spectrum an infinite number of levels would be required. The scaling function filters the lowest level of the transform and ensures all the spectrum is covered. See [link] for a detailed explanation.
For a wavelet with compact support, [\phi (t)] can be considered finite in length and is equivalent to the scaling filter g.
Meyer wavelets can be defined by scaling functions
Mexican hat wavelets can be defined by a wavelet function.
One use of wavelets is in data compression. Like several other transforms, the wavelet transform can be used to transform raw data (like images), then encode the transformed data, resulting in effective compression. JPEG 2000 is an image standard that uses wavelets. For details see wavelet compression.
From Wikipedia, the Free Encyclopedia. Original article here. Support Wikipedia by contributing or donating.
Some example mother wavelets are:
For the continuous WT, the pair (a,b) varies over the full half-plane [\R_+\times\R]; for the discrete WT this pair varies over a discrete subset of it, which is also called affine group.Comparisons with Fourier
The wavelet transform is often compared with the Fourier transform, in which signals are represented as a sum of sinusoids. The main difference is that wavelets are localized in both time and frequency whereas the standard Fourier transform is only localized in frequency. The Short-time Fourier transform (STFT) is also time and frequency localized but there are issues with the frequency time resolution and wavelets often give a better signal representation using Multiresolution analysis.Definition of a wavelet
There are a number of ways of defining a wavelet (or a wavelet family).Scaling filter
The wavelet is entirely defined by the scaling filter g - a low-pass finite impulse response (FIR) filter of length 2N and sum 1. In biorthogonal wavelets, separate decomposition and reconstruction filters are defined.Scaling function
Wavelets are defined by the wavelet function [\psi (t)] (i.e. the mother wavelet) and scaling function [\phi (t)] (also called father wavelet) in the time domain.Wavelet function
The wavelet only has a time domain representation as the wavelet function [\psi (t)].Applications
Generally, the DWT is used for source coding whereas the CWT is used for signal analysis. Consequently, the DWT is commonly used in engineering and computer science and the CWT is most often used in scientific research. Wavelet transforms are now being adopted for a vast number of different applications, often replacing the conventional Fourier transform. Many areas of physics have seen this paradigm shift, including molecular dynamics, ab initio calculations, astrophysics, density-matrix localisation, seismic geophysics, optics, turbulence and quantum mechanics. Other areas seeing this change have been image processing, blood-pressure, heart-rate and ECG analyses, DNA analysis, protein analysis, climatology, general signal processing, speech recognition, computer graphics and multifractal analysis. In computer vision and image processing, the notion of scale-space representation and Gaussian derivative operators is regarded as a canonical multi-scale representation.History
The development of wavelets can be linked to several separate trains of thought, starting with Haar's work in the early 20th century. Notable contributions to wavelet theory can be attributed to Goupillaud, Grossman and Morlet's formulation of what is now known as the CWT (1982), Strömberg's early work on discrete wavelets (1983), Daubechies' orthogonal wavelets with compact support (1988), Mallat's multiresolution framework (1989), Delprat's time-frequency interpretation of the CWT (1991), Newland's Harmonic wavelet transform and many others since.Time line
Wavelet transforms
There are a large number of wavelet transforms each suitable for different applications. For a full list see list of wavelet-related transforms but the common ones are listed below:List of wavelets
Discrete wavelets
See also
References
External links
Wavelets made Simple http://www.ee.ryerson.ca/~jsantarc/html/theory.html
All text is available under the terms of the GNU Free Documentation License See Wikipedia Copyrights for details.