| 3.6. Filter NL | ||
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3. Enhance Filters |  |
| 3.6. Filter NL | ||
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3. Enhance Filters | |
NL means “Non Linear”. Derived from the Unix pnmnlfilt program, it joins smoothing, despeckle and sharpen enhancement functions. It works on the whole layer, 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.
You can find this filter through → → .
The filter does not work if the active layer has an alpha channel. Then the menu entry is insensitive and grayed out.
When checked, parameter setting results are interactively displayed in preview.
The Operating Mode is described below.
Controls the amount of the filter to apply. Valid range is
0.00-1.00. The exact meaning of this value depends on the selected
operating mode. Note that this parameter is related to but not
the same as the alpha parameter used in the
pnmnlfilt program.
Controls the size of the effective sampling region around each pixel. The range of this value is 0.33-1.00, where 0.33 means just the pixel itself (and thus the filter will have no effect), and 1.00 means all pixels in the 3x3 grid are sampled.
This filter can perform several distinct functions:
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 (i.e. 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 1.0 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 = 0.8,
Radius = 0.6.
For a more blatant effect try
Alpha = 1.0,
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
values like
Alpha = 0.2,
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.
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 (i.e. turning .gif
files back into 24 bit files) you could try a pass of the optimal
estimation filter (Alpha = 0.2,
Radius = 1.0), a pass of the median filter
(Alpha = 1.0, 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 trade-off between filtering effectiveness and losing detail.
Experimentation is encouraged.
