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)

add_noise_distribution: 入力 → 出力

*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).*

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.

Try it in Studio

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.

add_noise_distribution 0.50 0.50

▸ Load this pipeline · Load & run

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