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diff --git a/pnmnlfilt.html b/pnmnlfilt.html new file mode 100644 index 00000000..401472c0 --- /dev/null +++ b/pnmnlfilt.html @@ -0,0 +1,178 @@ +<!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 "alpha" parameter selects among the three +modes. + +<H3 id="alphatrimmedmean"> +Alpha trimmed mean filter (0.0 <= alpha <= 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 "strength" 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 "pop" 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 "pop" 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 <= alpha <= 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 "wanted" 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 >= alpha >= -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&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 +"Converting Dithered Images Back to Gray Scale" by Allen +Stenger, Dr Dobb's Journal, November 1992, and this article references +"Digital Image Enhancement and Noise Filtering by Use of Local +Statistics", 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 "xim" program, which in turn took it from +section 6 of "Digital Halftones by Dot Diffusion", +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 <= alpha <= 0.5)</A> + <LI><A HREF="#optimalestsmooth"> + Optimal estimation smoothing. (1.0 <= alpha <= 2.0)</A> + <LI><A HREF="#edgeenhance"> + Edge enhancement. (-0.1 >= alpha >= -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> |