xsp_savgol — 2D `smoothing` op

• Data kinds: image → image

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

xsp_savgol: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

Usage

Smooths the image with a Savitzky-Golay filter (applied one-dimensionally, column direction first then row direction).

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

`a は窓幅を 5, 7, 9, 11, 13 の奇数に振る（w = 5 + 2*int(a*4)`）。

多項式次数は 2 に固定。`b` は未使用。窓内に 2 次多項式を最小二乗

フィットして中心値を置き換える手法なので、単純な移動平均よりピークの

高さや勾配を保ったまま平滑化できる。窓幅が画像サイズに対して大きすぎると

境界の処理（`scipy` の既定の外挿）の影響が強く出る。

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.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

xsp_savgol 0.35 0.50

▸ Load this pipeline · Load & run

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_savgol — 2D smoothing op