xsk3_rank_subtract_mean — 2D gray op

Data kinds: imageimage

Call: fullseye.apply(img, "xsk3_rank_subtract_mean", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

xsk3_rank_subtract_mean: 入力 → 出力

*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).*

Usage

Local mean subtraction (skimage `filters.rank.subtract_mean`). Returns the difference obtained by subtracting the mean brightness of the disk neighborhood from each pixel. The skimage implementation scales the difference to 1/2 and shifts it to the middle of the range, to avoid underflow. It cancels global light/dark unevenness and enhances local contrast.

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

`a は円盤半径(1+int(a*4) で 1〜5)を振る。b` は未使用。8bit 量子化を経由するため元の float64 精度は失われる。

Detailed usage guide

gallery2d_gray_arith 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.

xsk3_rank_subtract_mean 0.50 0.50

▸ Load this pipeline  ·  Load & run

Runnable examples (verified samples that actually call this op)

gallery2d_gray_arithpy -3.11 examples/gallery2d_gray_arith.py

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

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

Same category (gray)

gamma · invert · scale_clip · equalize · sigmoid · clahe · sk_adapthist · sk_enhance_contrast


*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.