coherence_enhancing_diff — 2D `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)

Usage

> This operator's description has not been translated yet. The original text follows as it is.

実装は `anisotropic_diffusion と同一(kind: "anisotropic"`)。

本来の coherence-enhancing diffusion は構造テンソルの固有ベクトルに沿って

拡散方向を制御する(線状構造をつなげる)手法だが、この代役ではその構造

テンソル計算を行わず、単純な Perona-Malik 異方性拡散で代用している

(近似の限界 ―― 線状構造の連結効果は再現されない)。HALCON の

`coherence_enhancing_diff`（Perform a coherence enhancing diffusion of

an image.）の代役。

`a が反復回数、b` が伝導度閾値 K を振る。両方が使われる。

Detailed usage guide

• gallery2d_smoothing_rank family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`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).*

coherence_enhancing_diff — 2D smoothing op