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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-

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 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

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.

edge enhancement --0 1 = alpha = -0 9-

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

optimal estimation smoothing -1 0 = alpha = 2 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.

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.

bugs

Integers and
tables may overflow if PPM_MAXMAXVAL is greater than
255.