smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "xcv3_pyr_laplacian", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*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).*
Sharpening via a Laplacian pyramid. A kind of unsharp masking that enhances the image by adding back the difference (one level of a Laplacian pyramid, corresponding to a band-pass component) obtained by subtracting, from the original image, the image restored via `pyrUp after being shrunk with pyrDown` (the low-frequency component).
> The detailed description below is the original text — the summary and the headings are translated.
`a は強調係数(0.5+2.5*a で 0.5〜3.0)を振る —— 大きいほど強くシャープになる(オーバーシュートも増える)。b` は未使用。結果は [0,1] にクリップ。
• gallery2d_smoothing_rank family guide
• 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.
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.
xcv3_pyr_laplacian 0.35 0.50
▸ Load this pipeline · Load & run
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.