anisotropic_diffusion — 2D `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)

Usage

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

Perona-Malik 型の異方性拡散。上下左右 4 方向の画素差にガウス型の

伝導度関数 `exp(-(Δ/K)^2)` を掛けて反復的に加算する ―― 差が閾値 K より

大きい(=エッジ)方向には拡散させず、小さい(=平坦)方向だけ滑らかにする

エッジ保存平滑化。HALCON の `anisotropic_diffusion`（Perform an

anisotropic diffusion of an image.）に相当。

`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。

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).*

anisotropic_diffusion — 2D smoothing op