xsp_cspline_smooth — 2D `smoothing` op

• Data kinds: image → image

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

xsp_cspline_smooth: 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

Cubic spline smoothing filter (`scipy.signal.cspline2d`).

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

`a は平滑化強度 lambda を 1.0〜11.0 に振る（lambda = 1.0 + 10.0*a、大きいほど強く均す）。b` は未使用。スプライン補間の平滑化項を直接使うフィルタで、ガウシアンぼかしに近い低域通過だが境界はミラー的に処理される。負の値を返しうる中間結果を最後に [0,1] にクリップしている。

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_cspline_smooth 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_cspline_smooth — 2D smoothing op