xcv3_pyr_laplacian — 2D `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])

Usage

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。

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).*

xcv3_pyr_laplacian — 2D smoothing op