smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "xcv_edge_preserving", 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 actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ xcv_edge_preserving: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ xcv_edge_preserving: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ xcv_edge_preserving: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
OpenCV's edgePreservingFilter (edge-preserving smoothing; `flags=1` = RECURS_FILTER = recursive filter method).
> The detailed description below is the original text — the summary and the headings are translated.
bilateral フィルタに近い効果をより高速に得る手法で、輪郭を保ちながら
内部を滑らかにする。
`a が空間方向の平滑化範囲 sigma_s を 20〜120 で振る。b` が
色差の許容範囲 `sigma_r` を 0.1〜0.6 で振る(大きいほど強く均す)。
Precision (measured): internally quantised to 8 bit (256 levels) — the output lands exactly on the k/255 grid. Passing float64 does not preserve that precision: a 16-bit camera's step (about 1.5e-5) is rounded to 1/255 = about 3.9e-3, and two images that differ by less than 1/255 return the same answer. Where faint intensity differences are being measured, or the output is fed on to differentiation or regression, the quantisation steps become visible — choose an op that does not pass through 8 bit.
• 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.
xcv_edge_preserving 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.