man > pnmnlfilt(1)

Pnmnlfilt User Manual(1)               General Commands Manual              Pnmnlfilt User Manual(1)

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  essen‐
       tially 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 con‐
       volution  (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 al‐
       pha 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, ra‐
       dius = 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 im‐
       ages.


   Edge enhancement. (-0.1 >= alpha >= -0.9)
       This is the opposite type of filter to the smoothing filter. It enhances edges. The alpha pa‐
       rameter 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 ]8;;index.html#commonoptions\ Common Options]8;;\ .)


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 adjust‐
       ment.

       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 AT labtam.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)