pnmnlfilt



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




NAME

       pnmnlfilt  - non-linear filters: smooth, alpha trim mean, optimal esti-
       mation 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 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),whichistaken-
       fromPhilip R. Thompson’s ’xim’ program, which in turn took it from sec-
       tion 6 of ’Digital Halftones by Dot Diffusion’, D. E. Knuth, ACM Trans-
       action 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(1)



AUTHOR

       Graeme W. Gill graeme@labtam.oz.au



netpbm documentation            5 February 1993       Pnmnlfilt User Manual(0)

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