Anisotropic Kuwahara Filtering with Polynomial Weighting Functions

In this work we present new weighting functions for the anisotropic Kuwahara filter. The anisotropic Kuwahara filter is an edge-preserving filter that is especially useful for creating stylized abstractions from images or videos. It is based on a generalization of the Kuwahara filter that is adapted to the local shape of features. For the smoothing process, the anisotropic Kuwahara filter uses weighting functions that use convolution in their definition. For an efficient implementation, these weighting functions are usually sampled into a texture map. By contrast, our new weighting functions do not require convolution and can be efficiently computed directly during the filtering in real-time. We show that our approach creates output of similar quality as the original anisotropic Kuwahara filter and present an evaluation scheme to compute the new weighting functions efficiently by using rotational symmetries.