dl_aniso_diffusion — 2D `smoothing` op

• Data kinds: image → image

• Call: fullseye.apply(img, "dl_aniso_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

A Perona-Malik-type anisotropic diffusion filter (torch implementation; runs on the GPU if one is available).

> The detailed description below is the original text — the summary and the headings are translated.

上下左右 4 方向の差分にそれぞれ `exp(-(差分/K)^2)` の伝導度を掛けてから加算する

反復拡散で、勾配が大きい（エッジらしい）場所ほど拡散が弱まり、エッジを保ったまま

平滑化する。`a はエッジ検出しきい値 K を 0.02〜0.22 に振る（`K = 0.02 +

0.2*a`、大きいほどエッジを跨いで拡散しやすくなる）。b` は反復回数を 5〜20 回に

振る（`iters = 5 + int(b*15)）。ステップ幅 lam=0.2` は固定。ガウシアンぼかしと

異なりエッジをぼかさずにノイズだけ均せるのが利点。HALCON の

`anisotropic_diffusion`（画像の異方性拡散を行う）に相当するが、同じ結果になる

とは限らない近似実装。

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).*

dl_aniso_diffusion — 2D smoothing op