smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "xpil_smooth_more", 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.*
*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ xpil_smooth_more: 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).*
Pillow's smoothing filter (strong version). Blurs more strongly than SMOOTH using the fixed kernel of PIL.ImageFilter.SMOOTH_MORE.
a, b are unused (fixed kernel).
The kernel is a 5x5 weighted mean (centre 44, its 8 neighbours 5, the outermost 16 pixels 1, divided by the total of 100). Because the centre weight is large, the blur is weaker than a uniform mean of the same 5x5; an impulse spreads only to about 44% at the centre + 5% each to the 8 adjacent pixels (measured: an impulse of 255 → centre 112, adjacent 13, outer ring 0). It acts more broadly than the 3x3 SMOOTH (centre 5, neighbours 1, total 13).
The input is clipped to [0,1] and turned into an 8-bit L image by truncating *255, and the output is converted back with /255 (introducing a 1/255 quantization). Pillow does not filter the outermost 2 pixels of the image, which keep their input values. A 0-size image is rejected with a ValueError and falls back fail-soft to the clipped input (recorded in the ledger). If you want a variable amount of blur, use gaussian or median. For removing noise before edge detection (xpil_contour /xpil_find_edges) or threshold.
• 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.
xpil_smooth_more 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.