add_noise_distribution — 2D `noise` op

• Data kinds: image → image

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

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

Usage

Adds additive noise to the image. The implementation supports only Gaussian (normal-distribution) noise, sharing the `gaussian branch of _sh_noise with add_noise_white. The random numbers are generated with a fixed seed determined by a (int(a*997)+7`), so the same a always produces the same noise (deterministic). b sets the noise standard deviation to 0.02-0.22.

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

HALCON の `add_noise_distribution`（任意の確率分布（ヒストグラム指定）に従うノイズを加える演算）とは異なり、この実装は常にガウス分布のノイズしか生成できない近似 —— 分布形状の指定は反映されない。

Detailed usage guide

• gallery2d_smoothing_rank family guide

Background guides (the physics and conventions behind this op)

• mv_image_sensors — 産業用イメージセンサ（現行品中心）

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 (`noise`)

add_noise_white

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

add_noise_distribution — 2D noise op