smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "sk_rolling_ball", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
Background subtraction by the rolling-ball method. Treats image brightness as terrain, rolls a ball of a given radius underneath it, estimates the resulting envelope surface as the background, and subtracts it from the original image -- used to remove uneven illumination (gradual brightness gradients).
> 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 `v - restoration.rolling_ball(v, radius=5+int(a*20)) を [0,1]` へ clip したもの —— a は球の半径を 5〜25 に振る(大きいほど緩やかな照明ムラしか背景とみなさず、細かい構造は残る)。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_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.