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NAME

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

SYNOPSIS

       pnmnlfilt alpha radius [pnmfile]

DESCRIPTION

       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  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 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
       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.

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.
       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.

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
       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.

       The  edge  enhancement  details  are from pgmenhance(1), 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.

SEE ALSO

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

BUGS

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

AUTHOR

       Graeme W. Gill    graeme@labtam.oz.au

                                5 February 1993                   pnmnlfilt(1)