dl_guided_filter — 2D `smoothing` op

• Data kinds: image → image

• Call: fullseye.apply(img, "dl_guided_filter", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: guided_filter (the HALCON reference is a useful guide to its meaning and parameters)

Usage

A self-guided filter (guided filter, with the guide image being the input itself; torch implementation).

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

局所窓内の線形回帰 `q = a_lin*I + b_lin` を箱フィルタで解くエッジ保存平滑化。

`a は窓半径 r を 1〜5 に振り（r = 1 + int(a*4)）、b` は正則化項

`eps を 0.001〜0.051 に振る（eps = 0.001 + 0.05*b`、大きいほど強く平滑化

されエッジ保存が弱まる）。バイラテラルフィルタの高速な代替として知られる手法で、

ガイド画像を入力自身にしているのでセルフガイド付きのエッジ保存平滑化として働く。

HALCON の `guided_filter`（画像のガイデッドフィルタリングを行う）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_guided_filter — 2D smoothing op