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pnmnlfilt(1)									     pnmnlfilt(1)

       pnmnlfilt  -  non-linear  filters:  smooth, alpha trim mean, optimal estimation smoothing,
       edge enhancement.

       pnmnlfilt alpha radius [pnmfile]

       This is something of a swiss army knife filter. It has 3 distinct operating modes. In  all
       of  the	modes  each  pixel in the image is examined and processed according to it and its
       surrounding pixels values. Rather than using the 9 pixels in a 3x3 block, 7 hexagonal area
       samples	are  taken,  the size of the hexagons being controlled by the radius parameter. A
       radius value of 0.3333 means that the 7 hexagons exactly fit into the  center  pixel  (ie.
       there  will  be	no  filtering  effect).  A  radius value of 1.0 means that the 7 hexagons
       exactly fit a 3x3 pixel array.

Alpha trimmed mean filter.    (0.0 <;= alpha <= 0.5)
       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

       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 fil-
       tering try starting with values of alpha = 0.4, radius = 0.6  For a  more  blatant  effect
       try alpha = 0.5, radius = 1.0

Optimal estimation smoothing. (1.0 <;= alpha <= 2.0)
       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 mean-
       ingful.	 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.

Edge enhancement. (-0.1 >;= alpha >= -0.9)
       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

Combination use.
       The  various modes of pnmnlfilt 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 fil-
       ter, 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.

       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 encour-

       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 adjust-

       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 refer-
       ences "Digital Image Enhancement and Noise Filtering by Use of Local Statistics", Jong-Sen
       Lee, IEEE Transactions on Pattern Analysis and Machine Intelligence, March 1980.

       The  edge enhancement details are from pgmenhance(1), which is taken from Philip R. Thomp-
       son'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.

       pgmenhance(1), pnmconvol(1), pnm(5)

       Integers and tables may overflow if PPM_MAXMAXVAL is greater than 255.

       Graeme W. Gill	 graeme@labtam.oz.au

					 5 February 1993			     pnmnlfilt(1)
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