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<H2><A NAME="SECTION002041000000000000000">
Introduction</A>
</H2>
For processing classical images the sampling is made in accordance
with Shannon's [<A
 HREF="node370.html#shannon">32</A>] well-known theorem.  The discrete
wavelet transform (DWT) can be derived from this theorem if we process
a signal which has a cut-off frequency.  For such images the frequency
band is always limited by the size of the camera aperture.

<P>
A digital analysis is provided by the discretisation of 
formula <A HREF="node310.html#eqn_wave">14.1</A>, with some simple considerations on the modification of
the wavelet pattern by dilation.  Usually the wavelet
function <IMG
 WIDTH="59" HEIGHT="44" ALIGN="MIDDLE" BORDER="0"
 SRC="img601.gif"
 ALT="$\psi^*(x)$">
has no cut-off frequency and it is
necessary to suppress the values outside the frequency band in order
to avoid aliasing effects.
We can work in Fourier space, computing the transform scale by
scale.  The number of elements for a scale can be reduced, if
the frequency bandwidth is also reduced.  This is possible only
for a wavelet which also has a cut-off frequency.  The
decomposition proposed by Littlewood and Paley [<A
 HREF="node370.html#littlewood">22</A>] provides a
very nice illustration of the reduction of elements scale by
scale.  This decomposition is based on an iterative dichotomy
of the frequency band.  The associated wavelet is well
localized in Fourier space where it allows a reasonable analysis to be made 
although not in the original space.  The search for a discrete
transform which is well localized in both spaces leads to 
multiresolution analysis.

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<ADDRESS>
<I>Petra Nass</I>
<BR><I>1999-06-15</I>
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