scaling functions
DESCRIPTION
Scaling functions. ‘or connect the dots’. Fix filter no restrictions yet:. FUND. DEFN:. Scaling Function. relates at two levels of resolution. Basic condition:. Examples so far:. Box:. Tent centered at :. Daubechies D4: does there exist ?. - PowerPoint PPT PresentationTRANSCRIPT
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Scaling functions ‘or connect the dots’
Fix filter no restrictions yet:
FUND. DEFN: Scaling Function
relates at two levels of resolution.
Basic condition:
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Examples so far:
Box:
Tent centered at :
Daubechies D4: does there exist ?
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Fractal example:
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Dyadic rationals:
determined at dyadic rationals :
Convolution on integers? Powers of 2?
KNOW ALL , THEN KNOW ALL
Construct on all as limiting fixed point!
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Iterative process: with limit
Construct sequence functions such that
Then
What about convergence? Pointwise, in Energy?
Pointwise: start with Tent function
In energy: start with Box function
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Getting started:
Tent function centered at origin:
for suitable
Basic idea: set
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Filter conditions:
Need in
so that
Conditions on :
Solve using Fourier Transforms as usual.
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Fourier Transforms:
Set
Then
So:
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Up-sampling again!
Recall
Crucial results:
where in z-transform notation:
Use these to compute .
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Connect the dots! Daub-4
Depths: 1, 2, 4, 6
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Cascade Algorithm: convergence in energy
Start with box function: can exploit orthonormality.
with
as before, but no Vetterli condition yet. So
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Orthonormality:
Case: k = 0
Can we recognize sequence:
?
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Finally Vetterli!
Consider first:
Crucial identification:
Fourier transform:
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Finally Vetterli!
When
we deduce that
So , hence
ORTHONORMAL FAMILY for each k.
,
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In the limit!
When
in energy, then
so Vetterli ensures
orthonormal family in .
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Finally wavelets:
Assume convergence in energy and Vetterli.
Fix FIR filter
Set define wavelet by
compactly supported if compactly supported.
By same argument as for :
So need to identify .
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More results for wavelets:
Recall
so,
By Vetterli yet again:so
Thus orthonormal family.
,
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Still more results:
By same argument yet again:
But by Fourier transforms yet again:
where remember,
Thus: all .
.
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Main Theorem Part 1:
FIR filter
If
then
is a continuous function with derivatives.
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Main Theorem Part 2:
Suppose also satisfies Vetterli condition.
Define wavelet:
Then:
,orthonormal families,
1.
2.
3.
complete orthonormal family in .
,
.
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so
hence Daub-4
continuous,
not quite differentiable
Main Theorem applied to Daub-4: