xpil_smooth_more — 2D smoothing op

Data kinds: imageimage

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])

xpil_smooth_more: input → output

*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).*

Usage

Pillow's smoothing filter (strong version). Blurs more strongly than `SMOOTH using the fixed kernel of PIL.ImageFilter.SMOOTH_MORE`.

> The detailed description below is the original text — the summary and the headings are translated.

a, b は未使用(固定カーネル)。

カーネルは 5x5 の重み付き平均(中心 44、その 8 近傍が 5、最外周 16 画素が1、合計 100 で割る)。中心の重みが大きいので同じ 5x5 の一様平均よりぼけは弱く、インパルスは中心 44% + 隣接 8 画素 5% ずつに広がる程度(実測: 255 のインパルス → 中心 112、隣接 13、外周 0)。3x3 の `SMOOTH`(中心 5、近傍 1、合計 13)よりは広く効く。

入力は [0,1] に clip して `*255 の切り捨てで 8 ビット L 画像にし、出力を /255 で戻す(1/255 の量子化が入る)。画像の最外周 2 画素はPillow がフィルタせず入力値のまま。0 サイズの画像は ValueError で拒否され、fail-soft で入力のクリップ版に落ちる(台帳に記録)。ぼかし量を可変にしたいなら gaussianmedian を使う。エッジ検出(xpil_contour /xpil_find_edges)や threshold` の前のノイズ落としに。

Detailed usage guide

gallery2d_smoothing_rank family guide

References (sample data, literature)

• 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.

Try it in Studio

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

Runnable examples (verified samples that actually call this op)

gallery2d_smoothing_rankpy -3.11 examples/gallery2d_smoothing_rank.py

Ops the type connects to (they accept image as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (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.