smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "sk_tv_bregman", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
Total variation denoising (TV denoising, Split-Bregman method). Uses the same TV-regularization idea as sk_tv but with a different optimization algorithm (Bregman splitting), said to converge faster.
> 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_tv_bregman(v, weight=1.0+8.0*a) を [0,1] へ clip したもの —— a は weight を 1.0〜9.0 に振るが、この関数の weight は**小さいほど強く平滑化される**(sk_tv の weight とは符号の向きが逆 —— denoise_tv_chambolle は大きいほど強く、denoise_tv_bregman` は小さいほど強い。混同すると意図と逆方向に a を動かすことになるので注意)。b は未使用。
• 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.
• 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_wavelet · sk_rolling_ball · sk_nlm
*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.