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| In the [[mathematics|mathematical]] topic of [[wavelet]] theory, the '''cascade algorithm''' is a [[numerical method]] for calculating function values of the basic [[Wavelet#Scaling_function|scaling]] and [[wavelet]] functions of a [[discrete wavelet transform]] using an iterative algorithm. It starts from values on a coarse sequence of sampling points and produces values for successively more densely spaced sequences of sampling points. Because it applies the same operation over and over to the output of the previous application, it is known as the ''cascade algorithm''.
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| == Successive approximation ==
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| The iterative algorithm generate successive approximations to ψ(''t'') or φ(''t'') from {''h''} and {''g''} filter coefficients. If the algorithm converges to a fixed point, then that fixed point is the basic scaling function or wavelet.
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| The iterations are defined by
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| : <math>\varphi^{(k+1)}(t)=\sum_{n=0}^{N-1} h[n] \sqrt 2 \varphi^{(k)} (2t-n)</math>
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| For the ''k''<sup>th</sup> iteration, where an initial φ<sup>(0)</sup>(''t'') must be given.
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| The frequency domain estimates of the basic scaling function is given by
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| : <math>\Phi^{(k+1)}(\omega)= \frac {1} {\sqrt 2} H\left( \frac {\omega} {2}\right) \Phi^{(k)}\left(\frac {\omega} {2}\right)</math>
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| and the limit can be viewed as an infinite product in the form
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| : <math>\Phi^{(\infty)}(\omega)= \prod_{k=1}^{\infty} \frac {1} {\sqrt 2} H\left( \frac {\omega} {2^k}\right) \Phi^{(\infty)}(0).</math>
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| If such a limit exists, the spectrum of the scaling function is
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| : <math>\Phi(\omega)= \prod_{k=1}^\infty \frac {1} {\sqrt 2} H\left( \frac {\omega} {2^k}\right) \Phi^{(\infty)}(0)</math>
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| The limit does not depends on the initial shape assume for φ<sup>(0)</sup>(''t''). This algorithm converges reliably to φ(''t''), even if it is discontinuous.
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| From this scaling function, the wavelet can be generated from
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| : <math>\psi(t)= \sum_{n=- \infty}^{\infty} g[n]{\sqrt 2} \varphi^{(k)} (2t-n).</math>
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| Plots of the function at each iteration is shown in Figure 1.
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| Successive approximation can also be derived in the frequency domain.
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| <!-- Missing image removed: [[File:Figure cascade.png|'''Figure 1. Iterations of the successive approximations of Daubechies wavelet and scale function by the cascade algorithm.''']] --> | |
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| == References ==
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| * [[C. Sidney Burrus|C.S. Burrus]], R.A. Gopinath, H. Guo, ''Introduction to Wavelets and Wavelet Transforms: A Primer'', Prentice-Hall, 1988, ISBN 0-13-489600-9.
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| * http://cnx.org/content/m10486/latest/
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| * http://cm.bell-labs.com/cm/ms/who/wim/cascade/index.html
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| [[Category:Wavelets]]
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Hi there. My name is Sophia Meagher even though it is not the name on my beginning certification. For a whilst I've been in Mississippi but now I'm considering other options. I am really fond of handwriting but I can't make it my occupation really. He works as a bookkeeper.
Also visit my homepage; certified psychics