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authorgiraffedata <giraffedata@9d0c8265-081b-0410-96cb-a4ca84ce46f8>2006-12-25 03:06:05 +0000
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+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 3.2//EN">
+<HTML><HEAD><TITLE>Pnmnlfilt User Manual</TITLE></HEAD>
+<BODY>
+<H1>pnmnlfilt</H1>
+Updated: 24 October 2006
+<BR>
+<A HREF="#index">Table Of Contents</A>
+
+<H2>NAME</H2>
+
+pnmnlfilt - non-linear filters: smooth, alpha trim mean, optimal
+estimation smoothing, edge enhancement.
+
+<H2 id="synopsis">SYNOPSIS</H2>
+
+<B>pnmnlfilt</B>
+<I>alpha</I>
+<I>radius</I>
+[<I>pnmfile</I>]
+
+<H2 id="description">DESCRIPTION</H2>
+
+<p>This program is part of <a href="index.html">Netpbm</a>.
+
+<p><B>pnmnlfilt</B> produces an output image where the pixels are a
+summary of multiple pixels near the corresponding location in an input
+image.
+
+<P>This program works on multi-image streams.
+
+<P>This is something of a swiss army knife filter.  It has 3 distinct
+operating modes.  In all of the modes <b>pnmnlfilt</b> examines each
+pixel in the image and processes it according to the values of it and
+its surrounding pixels.  Rather than using a square block of
+surrounding pixels (e.g. the subject pixel and its 8 immediate
+neighbors, in a 3x3 square), <b>pnmnlfilt</b> uses 7 hexagonal areas.
+You choose the size of the hexagons with the radius parameter.  A
+radius value of 1/3 means that the 7 hexagons essentially fit into the
+subject pixel (ie.  there will be no filtering effect).  A radius
+value of 1.0 means that the 7 hexagons essentially cover the 3x3
+immediate neighbor square.
+
+<p>Your choice of &quot;alpha&quot; parameter selects among the three
+modes.
+
+<H3 id="alphatrimmedmean">
+Alpha trimmed mean filter (0.0 &lt;= alpha &lt;= 0.5)</H3>
+
+<P>The value of the center pixel will be replaced by the mean of
+the 7 hexagon values, but the 7 values are sorted by size and the top
+and bottom alpha portion of the 7 are excluded from the mean.  This
+implies that an alpha value of 0.0 gives the same sort of output as a
+normal convolution (ie. averaging or smoothing filter), where radius
+will determine the &quot;strength&quot; of the filter. A good value to
+start from for subtle filtering is alpha = 0.0, radius = 0.55 For a
+more blatant effect, try alpha 0.0 and radius 1.0
+
+<P>An alpha value of 0.5 will cause the median value of the 7 hexagons
+to be used to replace the center pixel value. This sort of filter is
+good for eliminating &quot;pop&quot; or single pixel noise from an
+image without spreading the noise out or smudging features on the
+image. Judicious use of the radius parameter will fine tune the
+filtering. Intermediate values of alpha give effects somewhere between
+smoothing and &quot;pop&quot; noise reduction. For subtle filtering
+try starting with values of alpha = 0.4, radius = 0.6 For a more
+blatant effect try alpha = 0.5, radius = 1.0
+
+<H3 id="optimalestsmooth">
+Optimal estimation smoothing. (1.0 &lt;= alpha &lt;= 2.0)</H3>
+
+<P>This type of filter applies a smoothing filter adaptively over the
+image.  For each pixel the variance of the surrounding hexagon values
+is calculated, and the amount of smoothing is made inversely
+proportional to it. The idea is that if the variance is small then it
+is due to noise in the image, while if the variance is large, it is
+because of &quot;wanted&quot; image features. As usual the radius
+parameter controls the effective radius, but it probably advisable to
+leave the radius between 0.8 and 1.0 for the variance calculation to
+be meaningful.  The alpha parameter sets the noise threshold, over
+which less smoothing will be done.  This means that small values of
+alpha will give the most subtle filtering effect, while large values
+will tend to smooth all parts of the image. You could start with
+values like alpha = 1.2, radius = 1.0 and try increasing or decreasing
+the alpha parameter to get the desired effect. This type of filter is
+best for filtering out dithering noise in both bitmap and color
+images.
+
+<H3 id="edgeenhance">Edge enhancement. (-0.1 &gt;= alpha &gt;= -0.9)</H3>
+
+<P>This is the opposite type of filter to the smoothing filter. It
+enhances edges. The alpha parameter controls the amount of edge
+enhancement, from subtle (-0.1) to blatant (-0.9). The radius
+parameter controls the effective radius as usual, but useful values
+are between 0.5 and 0.9. Try starting with values of alpha = 0.3,
+radius = 0.8
+
+<H3 id="combination">Combination use.</H3>
+
+<P>The various modes of <B>pnmnlfilt</B> can be used one after the
+other to get the desired result. For instance to turn a monochrome
+dithered image into a grayscale image you could try one or two passes
+of the smoothing filter, followed by a pass of the optimal estimation
+filter, then some subtle edge enhancement. Note that using edge
+enhancement is only likely to be useful after one of the non-linear
+filters (alpha trimmed mean or optimal estimation filter), as edge
+enhancement is the direct opposite of smoothing.
+
+<P>For reducing color quantization noise in images (ie. turning .gif
+files back into 24 bit files) you could try a pass of the optimal
+estimation filter (alpha 1.2, radius 1.0), a pass of the median filter
+(alpha 0.5, radius 0.55), and possibly a pass of the edge enhancement
+filter.  Several passes of the optimal estimation filter with
+declining alpha values are more effective than a single pass with a
+large alpha value.  As usual, there is a tradeoff between filtering
+effectiveness and loosing detail. Experimentation is encouraged.
+
+<H2 id="references">References:</H2>
+
+<P>The alpha-trimmed mean filter is based on the description in IEEE
+CG&amp;A May 1990 Page 23 by Mark E. Lee and Richard A. Redner, and
+has been enhanced to allow continuous alpha adjustment.
+
+<P>The optimal estimation filter is taken from an article
+&quot;Converting Dithered Images Back to Gray Scale&quot; by Allen
+Stenger, Dr Dobb's Journal, November 1992, and this article references
+&quot;Digital Image Enhancement and Noise Filtering by Use of Local
+Statistics&quot;, Jong-Sen Lee, IEEE Transactions on Pattern Analysis
+and Machine Intelligence, March 1980.
+
+<P>The edge enhancement details are from <A
+HREF="pgmenhance.html">pgmenhance</A>, which is taken from Philip
+R. Thompson's &quot;xim&quot; program, which in turn took it from
+section 6 of &quot;Digital Halftones by Dot Diffusion&quot;,
+D. E. Knuth, ACM Transaction on Graphics Vol. 6, No. 4, October 1987,
+which in turn got it from two 1976 papers by J. F. Jarvis et. al.
+
+<h2 id="parameters"></h2>
+
+<p>The parameters are:
+
+<dl>
+<dt><i>alpha</i>
+<dd>The alpha value (described above), in decimal.  May be fractional.
+
+<dt><i>radius</i>
+<dd>The radius (described above), in decimal.  May be fractional.
+</dl>
+
+<H2 id="seealso">SEE ALSO</H2>
+
+<A HREF="pgmenhance.html">pgmenhance</A>,
+<A HREF="pnmconvol.html">pnmconvol</A>,
+<A HREF="pnm.html">pnm</A>
+
+<H2 id="author">AUTHOR</H2>
+
+Graeme W. Gill <A HREF="mailto:graeme@labtam.oz.au">graeme@labtam.oz.au</A>
+
+<HR>
+<H2 id="index">Table Of Contents</H2>
+<UL>
+<LI><A HREF="#synopsis">SYNOPSIS</A>
+<LI><A HREF="#description">DESCRIPTION</A>
+<ul>
+  <LI><A HREF="#alphatrimmedmean">
+      Alpha trimmed mean filter.(0.0 &lt;= alpha &lt;= 0.5)</A>
+  <LI><A HREF="#optimalestsmooth">
+       Optimal estimation smoothing. (1.0 &lt;= alpha &lt;= 2.0)</A>
+  <LI><A HREF="#edgeenhance">
+      Edge enhancement. (-0.1 &gt;= alpha &gt;= -0.9)</A>
+  <LI><A HREF="#combination">Combination use.</A>
+  </ul>
+<LI><A HREF="#references">References:</A>
+<LI><A HREF="#seealso">SEE ALSO</A>
+<LI><A HREF="#author">AUTHOR</A>
+</UL>
+</BODY>
+</HTML>