mean_box — 2D `smoothing` op

• Data kinds: image → image

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

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

Usage

Simple averaging (box) filter over a rectangular window. Equivalent to HALCON's `mean_image` (Smooth by averaging.).

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

`a が窓の一辺を 3,5,7,9 の4段階（_k(a)、a を4分割して丸める）に切り替える。b` は未使用。ガウシアンより計算は軽いがリンギングが出やすく、エッジがぼやける。

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.

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 · bilateral · unsharp · median · min_filter · max_filter · percentile

Same category (`smoothing`)

gaussian · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm · sk_tv_bregman

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

mean_box — 2D smoothing op