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 figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
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.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
coherence_enhancing_diff 0.35 0.50
▸ Load this pipeline · Load & run
• 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.