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<H3><A NAME="SECTION0020921300000000000000">
FILTER/WAVE</A>
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<DIV ALIGN="CENTER">
FILTER/WAVE I_In I_Out [Algo] [T_Filter] [Iter_Nbr] [N_Scale] [N_Sigma] [Noise]
</DIV>
Filters an image in the wavelet space (see section
<A HREF="node328.html#sec_filter">14.6</A>). The used algorithm for the transform depends on the
parameter <EM>Algo</EM> (see TRANSF/WAVE). <EM>T_Filter</EM> defines the
type of filtering. It can take the values 1,2,3,4 (default value is
1):
<DL COMPACT>
<DT>1.
<DD>Thresholding (see section <A HREF="node332.html#sec_filt_1">14.6.4</A>).
<DT>2.
<DD>Hierarchical Thresholding (see section <A HREF="node333.html#sec_filt_2">14.6.5</A>).
<DT>3.
<DD>Hierarchical Wiener Filtering (see section <A HREF="node331.html#sec_filt_3">14.6.3</A>).
<DT>4.
<DD>Multiresolution Wiener Filtering (see section <A HREF="node330.html#sec_filt_4">14.6.2</A>).
</DL>If we threshold (<EM>T_Filter</EM> = 1 or 2), the reconstruction can be done
iteratively, and the parameter <EM>Iter_Nbr</EM> specifies the number of
iterations (by default it is 1, no iteration). <EM>N_Scale</EM> is the
number of scales (default value is 4). <EM>N_Sigma</EM> is used only if we threshold. We consider
that at a given scale <I>j</I>, the significant level <I>L</I> is:
<BR><P></P>
<DIV ALIGN="CENTER">
<!-- MATH: \begin{displaymath}
L = \mbox{N\_Sigma}.\sigma_j
\end{displaymath} -->
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SRC="img882.gif"
ALT="\begin{displaymath}L = \mbox{N\_Sigma}.\sigma_j\end{displaymath}">
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<P></P>
where <IMG
WIDTH="27" HEIGHT="39" ALIGN="MIDDLE" BORDER="0"
SRC="img883.gif"
ALT="$\sigma_j$">
is the standard deviation of the noise at the scale
<I>j</I>. <EM>Noise</EM> is the standard deviation of the noise in the
image. If <EM>Noise</EM> equal to 0 (0 is the default value), the
standard deviation is estimated automatically in the program from the
histogram of the image by a 3<IMG
WIDTH="19" HEIGHT="21" ALIGN="BOTTOM" BORDER="0"
SRC="img884.gif"
ALT="$\sigma$">
clipping.
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<ADDRESS>
<I>Petra Nass</I>
<BR><I>1999-06-15</I>
</ADDRESS>
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