This filter is found in
NL means "Non Linear". Derived
from the Unix pnmnlfilt program, it joins
smoothing, despeckle and sharpen enhancement functions. It works
on the whole image, not on the selection.
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 9 pixels in a 3x3 block, it uses an
hexagonal block whose size can be set with the Radius option.
“NL Filter” options
When Do preview is checked, parameter
setting results are interactively displayed in preview.
Alpha: Meaning of this value
depends on the selected option.
Controls the strength of the filter (0.33-1.00).
This filter can perform several distinct functions, depending on
the value of the parameter alpha.
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
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 .
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
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
The various operating modes can be used one after the other to
get the desired result. For instance to turn a monochrome
dithered image into 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.