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])
*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).*
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.
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_tv_bregman 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_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.