xsp_wiener — 2D `smoothing` op

• Data kinds: image → image

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

Usage

Suppresses noise using the Wiener adaptive filter (`scipy.signal.wiener`).

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

`a は近傍窓のサイズを 3, 5, 7, 9 の奇数に振る（k = 3 + 2*int(a*3)`、

a=0 で 3、a に近い 1 で 9）。`b` は未使用。局所分散からノイズ分散を

自動推定する適応フィルタで、一様な強さで均すガウシアン/ミーンボックスと

違い平坦な領域ほど強く均し、分散の大きい（エッジ/テクスチャの多い）領域は

保存されやすい。ガウス性ノイズを仮定するので塩胡椒ノイズには不向き。

出力は [0,1] にクリップする。

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).*

xsp_wiener — 2D smoothing op