Kuwahara filter
However the Kuwahara filter is able to apply smoothing on the image while preserving the edges.
It is named after Michiyoshi Kuwahara, Ph.D., who worked at Kyoto and Osaka Sangyo Universities in Japan, developing early medical imaging of dynamic heart muscle in the 1970s and 80s.
is a grey scale image and that we take a square window of size
The location of the pixel in relation to an edge plays a great role in determining which region will have the greater standard deviation.
On the other hand, should the pixel be on the lighter side of an edge it will most probably take a light value.
On the event that the pixel is located on the edge it will take the value of the more smooth, least textured region.
The fact that the filter takes into account the homogeneity of the regions ensures that it will preserve the edges while using the mean creates the blurring effect.
The size of the window is chosen in advance and may vary depending on the desired level of blur in the final image.
Typically windows are chosen to be square with sides that have an odd number of pixels for symmetry.
Additionally, the subregions do not need to overlap or have the same size as long as they cover all of the window.
To overcome this problem, for color images a slightly modified Kuwahara filter must be used.
The modified filter then operates on only the "brightness" channel, the Value coordinate in the HSV model.
[2] Originally the Kuwahara filter was proposed for use in processing RI-angiocardiographic images of the cardiovascular system.
[3] The fact that any edges are preserved when smoothing makes it especially useful for feature extraction and segmentation and explains why it is used in medical imaging.
The Kuwahara filter however also finds many applications in artistic imaging and fine-art photography due to its ability to remove textures and sharpen the edges of photographs.
The level of abstraction helps create a desirable painting-like effect in artistic photographs especially in the case of the colored image version of the filter.
These applications have known great success and have encouraged similar research in the field of image processing for the arts.
Although the vast majority of applications have been in the field of image processing there have been cases that use modifications of the Kuwahara filter for machine learning tasks such as clustering.
Several variations have been proposed for similar use most of which attempt to deal with the drawbacks of the original Kuwahara filter.
The "Generalized Kuwahara filter" proposed by P. Bakker[7] considers several windows that contain a fixed pixel.
This filter is not characterized by the same ambiguity in the presence of noise and manages to eliminate the block artifacts.
In comparison with the standard Kuwahara filter, both the objects and the edges retain a better quality.
As opposed to the standard Kuwahara filter, the window size is changing, depending on the local properties of the image.
For each of the four basic areas surrounding a pixel, the mean and variance are calculated.
