smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "anisotropic_diffusion", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: anisotropic_diffusion (the HALCON reference is a useful guide to its meaning and parameters)
Perona-Malik type anisotropic diffusion. Pixel differences in the four directions (up/down/left/right) are multiplied by a Gaussian-type conductance function `exp(-(delta/K)^2) and added iteratively -- this is edge-preserving smoothing that does not diffuse in directions where the difference exceeds threshold K (= an edge), and smooths only in directions where the difference is small (= flat). Corresponds to HALCON's anisotropic_diffusion` (Perform an anisotropic diffusion of an image).
> The detailed description below is the original text — the summary and the headings are translated.
`a が反復回数を 2〜10 回の範囲で、b` が伝導度の閾値 K を 0.05〜0.3
の範囲で振る。両方が使われる。K が小さいほどエッジを厳しく保護する。
• gallery2d_smoothing_rank family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
smoothing)gaussian · mean_box · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.