Pnmnlfilt User Manual(0)			      Pnmnlfilt User Manual(0)



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


SYNOPSIS
       pnmnlfilt alpha radius [pnmfile]


DESCRIPTION
       This program is part of Netpbm(1).

       pnmnlfilt produces an output image where the pixels are	a  summary  of
       multiple pixels near the corresponding location in an input image.

       This program works on multi-image streams.

       This  is	 something  of	a  swiss army knife filter.  It has 3 distinct
       operating modes.	 In all of the modes pnmnlfilt examines each pixel  in
       the  image  and processes it according to the values of it and its sur-
       rounding pixels.	 Rather than using a square block of surrounding  pix-
       els  (e.g.  the	subject	 pixel and its 8 immediate neighbors, in a 3x3
       square), pnmnlfilt 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.

       Your choice of 'alpha' parameter selects among the three modes.


   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 filtering 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 propor-
       tional 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.


   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 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.

       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.  Sev-
       eral passes of the optimal estimation filter with declining alpha  val-
       ues 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.


References:
       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.

       The  optimal  estimation	 filter	 is  taken from an article 'Converting
       Dithered Images Back to Gray Scale' by Allen Stenger, Dr	 Dobb's	 Jour-
       nal, November 1992, and this article references 'Digital Image Enhance-
       ment 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),whichistaken-
       fromPhilip R. Thompson's 'xim' program, which in turn took it from sec-
       tion 6 of 'Digital Halftones by Dot Diffusion', D. E. Knuth, ACM Trans-
       action  on  Graphics  Vol. 6, No. 4, October 1987, which in turn got it
       from two 1976 papers by J. F. Jarvis et. al.




       The parameters are:
       alpha  The alpha value (described above), in  decimal.	May  be	 frac-
	      tional.


       radius The radius (described above), in decimal.	 May be fractional.



SEE ALSO
       pgmenhance(1), pnmconvol(1), pnm(1)


AUTHOR
       Graeme W. Gill graeme@labtam.oz.au



netpbm documentation		24 October 2006	      Pnmnlfilt User Manual(0)