xpil_mode_filter — 2D `rank` op

• Data kinds: image → image

• Call: fullseye.apply(img, "xpil_mode_filter", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Mode filter. Uses `PIL.ImageFilter.ModeFilter` to replace each pixel with the most frequent value within the window (differs from a median filter in taking the mode rather than the median).

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

a が窓サイズを 3/5/7/9 の 4 段階（`3 + 2*int(a*3)`）で振る。b は未使用。単色の塗りつぶし領域が多い画像（ラベル画像・漫画調画像）のノイズ除去に向き、階調が滑らかな自然画像では効果が薄い。

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.

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -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 (`rank`)

median · min_filter · max_filter · percentile · sk_median_disk · cv_median · median_image · median_rect

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

xpil_mode_filter — 2D rank op