pnmnlfilt(1)						      General Commands Manual						      pnmnlfilt(1)

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  nor-
       mal  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 enhance-
       ment, 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 estima-
       tion 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 fil-
       ter.  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)