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Kuwahara Filter
One approach to enhancing the visibility of steps and making their location more precise operates by transforming gradual changes in brightness that extend over several pixels into an abrupt change. A useful analogy is the buttes and mesas in the American southwest, which rise with sheer walls from the desert floor. Erosion causes these walls to slump. In the same way, the finite size of pixels in the camera averages brightness values from both sides of a step, producing an intermediate value. Reversing this effect can restore the step sharpness. Called a maximum likelihood technique, this procedure compares the statistical properties of neighborhoods around each pixel and assigns to the pixel the average value of the neighborhood that it is most like. This interactive tutorial illustrates the application of the Kuwahara maximum likelihood filters to sharpen steps and edges in an image.
The tutorial initializes with a randomly selected specimen appearing in the Specimen Image window. The Choose A Specimen pull-down menu provides a selection of specimen images, in addition to the initial randomly chosen one. Adjacent to the Specimen Image window is the Output Image window showing the effect of applying a Kuwahara filter to the specimen. For color images, the processing is applied only to the pixel brightness values, thus retaining the original color information.
Contributing Authors
John C. Russ - Materials Science and Engineering Dept., North Carolina State University, Raleigh, North Carolina, 27695.
Matthew Parry-Hill, and Michael W. Davidson - National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310.
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