pnmnlfilt(1)

NAME

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

SYNOPSIS

       pnmnlfilt alpha radius [pnmfile]

DESCRIPTION

       This is something of a swiss army knife filter. It has 3 distinct oper-
       ating  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  parame-
       ter.  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

       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

       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),  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)