Kuwahara Node#
The Kuwahara node implements the Kuwahara filter as well as its anisotropic variant. The Kuwahara filter is a smoothing filter that tries to preserve the edges in the image. The smoothing effect of the anisotropic variant is similar to brush strokes, so the node can be used to create stylized painting effects.
輸入#
- 圖像
Standard color input.
- Size
Controls the size of the smoothing neighborhood. Large values may introduce artifacts for highly detailed areas. For the anisotropic method, the larger the size, the slower the filter.
屬性#
- Type
- Classic:
A simple smoothing method that averages the local square neighborhood of the image while preserving edges. Produces blocky results due to the square neighborhood and provides no tuning parameters, but is faster to compute.
- Anisotropic:
A complex smoothing method that averages the local neighborhood of the image in the direction of the flow of the edges, thus preserving the edges in the output. Produces painterly-like results and provides multiple turning parameters, while being slower to compute.
- High Precision
Uses a more precise but slower method. Use if the output contains undesirable noise.
- Uniformity
Controls the uniformity of the directions of the edges of the image. Non uniform directions are nearly never desirable, so this should typically be increased until the user notices the result is no longer changing in a significant way. Further increases would produces worst results and increase compute time.
- Sharpness
Controls the sharpness of the edges of the image.
- Eccentricity
Controls how thin and directional the filter is. Low eccentricity corresponds to circular omnidirectional features while high eccentricity corresponds to thin directional features.
輸出#
- 圖像
Standard color output.
Notes#
- Iterations
The filter can be applied multiple times by chaining the node multiple times. This chaining can produce more flat filtering.
- Performance
The filter can be expensive to compute for high size input and high resolution images. To improve performance, consider scaling down the image, applying the filter, then scaling it up again. This can work well because the filter already attenuates low frequency details.
