Gaussian blur and unsharp mask chapters

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homm 2014-12-01 02:18:11 +03:00
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Gaussian blur and unsharp mask Gaussian blur and unsharp mask
------------------------------ ------------------------------
:py:meth:`~PIL.ImageFilter.GaussianBlur` implementation was replaced with
sequential applying of series of box filters. New implementation is based on
"Theoretical foundations of Gaussian convolution by extended box filtering" from
Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
implementations uses Gaussian blur internally, all changes from this chapter
alse applyable to it.
Blur radius Blur radius
^^^^^^^^^^^ ^^^^^^^^^^^
There was an error in previous version of PIL, when blur radius (the standard
deviation of Gaussian) is actually meant blur diameter.
For example for blurring image with actual radius 5 you were forced
to use value 10. This was fixed. For now the meaning of the radius
is the same as in other software.
If you used a Gaussian blur with some radius value, you need to devide this
value by two.
Blur Performance Blur Performance
^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^
Box filter computation time is constant relative to the radius and depends
on source image size only. Because new Gaussian blur implementation
is based on box filter, it's computation time is also doesn't depends on blur
radius.
If before execution time for the same test image was 1 second for radius 1,
3.6 seconds for radius 10, 17 seconds for 50. Now blur with any radius on same
image is executed for 0.2 seconds.
Blur quality Blur quality
^^^^^^^^^^^^ ^^^^^^^^^^^^
Previous implementation takes in account only source pixels within
2 * standard deviation radius for every destination pixel. This was not enought,
so qulity was worse compared to other Gaussian blur software.
The new implementation does not have this drawback.