The task of identifying the properties of an acquired signal starts
with its given representation as an element in the reference
(i.e. fiducial, central) representation space
. One
singles out the large overall features by projecting it onto the next
subspace
. This projection process suppresses the finer
details of the signal. They are no longer present when the signal is
represented as an element of
. Using the pyramid algorithm
one repeats this process iteratively. In this process one moves from
the resolution
of
to the lower resolution
of
To keep track of the finer details suppressed by this process, one
introduces
, the orthogonal complement of
in
:
Thus any signal
In brief,
The o.n. bases for
and
, and hence
the representations of the signal
in these spaces, are known and
expressed in terms of the scaling function
. These bases
determine a unique basis for
whose purpose is to keep
track of the suppressed
of the signal
. The
process of constructing the
-basis resembles that
for
and
. One starts with a
square-integrable function
, the ``mother wavelet''. By
applying translations and dilations to it, one obtains the desired
o.n. basis for
, the space of details at resolution
. The crucial part of this endeavor is the construction of
the mother wavelet from the scaling function of the MSA.
The construction is done by means of the following theorem by Mallat:
be the hierarchy of vector spaces which make up the MSA whose scaling function is
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where
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The validation of this theorem is a three step process.
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with
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,
This is not the only constraint that
is equivalent to
Thus the filter functions
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and
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| (2122) | ||
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is unitary. These constraints are useful if for no other reasons than that they (i) place the two sequences of Fourier coefficients
This relation is not unique. Other possibilities are
Each side of this equation is a Fourier series in powers of
With both
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and the mother wavelet
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, i.e. point out why,
whenever [references_for_chapter2] [plain]