sk_wavelet — 2D `smoothing` op

• Data kinds: image → image

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

Usage

Denoising via wavelet transform. Decomposes the image into the wavelet domain, discards small coefficients (regarded as noise) using BayesShrink-criterion thresholding, and then applies the inverse transform.

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

HALCON に直接対応するものは無い。実装は `restoration.denoise_wavelet(v) をそのまま [0,1]` に clip したもので、しきい値やウェーブレット基底はすべて skimage の既定値まかせ —— a, b は未使用(調整点が無い、最も枯れた設定でとりあえずノイズを落としたい時用)。

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_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).*

sk_wavelet — 2D smoothing op