smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "coherence_enhancing_diff", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: coherence_enhancing_diff (the HALCON reference is a useful guide to its meaning and parameters)
The implementation is identical to `anisotropic_diffusion (kind: "anisotropic"). True coherence-enhancing diffusion is a method that controls the diffusion direction along the eigenvectors of the structure tensor (connecting linear structures), but this substitute does not compute the structure tensor and instead uses plain Perona-Malik anisotropic diffusion (a limitation of the approximation -- the effect of connecting linear structures is not reproduced). A stand-in for HALCON's coherence_enhancing_diff` (Perform a coherence enhancing 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、b` が伝導度閾値 K を振る。両方が使われる。
• 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.