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)