smooth_image — 2D smoothing op

• Data kinds: image → image

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

• HALCON equivalent: smooth_image (the HALCON reference is a useful guide to its meaning and parameters)

Usage

General-purpose smoothing approximated by a single Gaussian blur (sigma `0.5+2.0*a). HALCON's smooth_image` (Smooth an image using various filters.) is a versatile operator that lets you choose among several filter types such as Deriche/Gauss/Mean, but here it is simplified to a single Gaussian type (a limitation of this approximation).

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

`a がシグマを 0.5〜2.5 の範囲で振る。b` は未使用。

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).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.