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])

*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).*
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)。
• gallery2d_smoothing_rank family guide
• 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.
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.
sk_wavelet 0.35 0.50
▸ Load this pipeline · Load & run
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.