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Pnmnlfilt User Manual(0)                              Pnmnlfilt User Manual(0)



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 pnmnl-
       filt  examines  each pixel in the image and processes it according to the values of it and its surrounding pix-
       els.  Rather than using a square block of surrounding pixels (e.g. the subject pixel and its 8 immediate neigh-
       bors,  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 imme-
       diate 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  fil-
       tering.  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 effec-
       tive 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 Intelli-
       gence, March 1980.

       The  edge  enhancement details are from pgmenhance(1),whichistakenfromPhilip 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  Graph-
       ics Vol. 6, No. 4, October 1987, which in turn got it from two 1976 papers by J. F. Jarvis et. al.



       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 graemeATlabtam.au



netpbm documentation            24 October 2006       Pnmnlfilt User Manual(0)