smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "sk_tv", 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, Chambolle's method). Smooths noise in flat regions while preserving edges - unlike median/Gaussian blur, it is characterized by not easily degrading the sharpness of contours.
> 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_chambolle(v, weight=0.02+0.3*a)` —— a は denoising weight を 0.02〜0.32 に振り、大きいほど強く平滑化される(この符号の向きは後述の sk_tv_bregman と逆なので混同注意)。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 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_wavelet · 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.