NAME

       pnmnlfilt - non-linear filters: smooth, alpha trim mean, optimal estimation 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
       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), 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  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 losing detail. Experimentation
       is encouraged.

OPTIONS

       There are no command line options defined specifically for  pnmnlfilt,  but  it  recognizes  the  options
       common to all programs based on libnetpbm (See  Common Options .)

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.

PARAMETERS

       The parameters are:

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

       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

DOCUMENT SOURCE

       This  manual  page was generated by the Netpbm tool 'makeman' from HTML source.  The master documentation
       is at

              http://netpbm.sourceforge.net/doc/pnmnlfilt.html

netpbm documentation                             24 October 2006                        Pnmnlfilt User Manual(1)