smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "xcv3_denoise_tvl1", 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 L1 denoising (OpenCV `cv2.denoise_TVL1`, Primal-Dual algorithm). A method based on total variation minimization that removes noise while preserving edges, leaving edges sharper than ordinary Gaussian smoothing.
> The detailed description below is the original text — the summary and the headings are translated.
`a は正則化項の重み lambda(0.3+2.7*a で 0.3〜3.0。小さいほど強く平滑化される)、b は反復回数(int(10+40*b)` で 10〜50)を振る。8bit 量子化を経由するため入力の float64 精度は失われる。
• 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.
xcv3_denoise_tvl1 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.