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The Showfoto Handbook
• Noise filter: Increasing the Noise filter parameter helps reducing artifacts. The Noise can
range from 0-1 but values higher than 0.1 are rarely helpful. When the Noise value is too low,
e.g. 0 the image quality will be horrible. A useful value is 0.03. Using a high value for the
Noise will even blur the image further.
• Gaussian Sharpness: This isthe radiusfor the Gaussian convolutionfilter. Use thisparameter
when your blurring is Gaussian (mostly due to previous blur filtering). In most cases you
should leave this parameter to 0, because it causes nasty artifacts. When you use non-zero
values you will probably have to increase the Correlation and/or Noisefilter parameters, too.
• Matrix size: This parameter determines the size of the transformation matrix. Increasing the
MatrixSize may give better results, especially when you have chosen large values for Circular
Sharpness or Gaussian Sharpness. Note that the plug-in will become very slow when you
select large valuesfor this parameter. In most cases you should select a value in the range 3-10.
• Save As and Load: these buttons are used to do just that. Any Refocus parameters that you
have set can be saved to the filesystem and loaded later.
• Defaults: this button resets all settings to default values.
Below, you can see few hints to help you work with the refocus plug-in:
• Preferrably perform all cropping, color and intensity curve corrections on the image before
using this plug-in.
• Otherwise use this plug-in before performing any other operations on the image. The reason
is that many operations on the image will leave boundaries that are not immediately visible
but that will leave nasty artifacts.
• When you are scanning images and compress them, e.g. to JPEG, you should use the plug-in
on the uncompressed image.
3.1.4.1.9 Refocus comparison with other techniques
Comparison to two other techniques frequently used to enhance images are:
• SharpenFilter
• UnsharpMask
Sharpening applies a small convolution matrix that increases the difference between a source
pixel and its immediate neighbors. FIR Wiener filtering is a more general technique because it
allows a much larger neighborhood and better parameterizations. Sharpening only works when
your images are very slightly blurred. Furthermore, for high values of the sharpening parameter
the results frequently looks ´´noisy´´. With FIR Wiener filtering this noise can be greatly reduced
by selecting higher values for the Correlation and Noise filter parameters.
Unsharp masking is another very popular image enhancement technique. From a mathematical
point of view its justification is a bit obscure but many people are very fond of it. The first step
is to create a blurred copy of the source image. Then the difference between the source image
and the blurred image is subtracted from the source image, hence the name unsharp masking.
If fact, unsharp masking is more of a contrast enhancement on the important image feature than
asharpening. It does not undo the aperture pattern interference of the camera diaphragm as
refocus does.
In general, unsharp masking produces better results than sharpening. This is probably caused
by the fact that unsharp masking uses a larger neighborhood than sharpening.
From a theoretical point of view unsharp masking must always introduce artifacts. Even under
optimal circumstances it can never completely undo the effect of blurring. For Wiener filtering
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