sk_enhance_contrast — 2D gray op

Data kinds: imageimage

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

sk_enhance_contrast: 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 contrast enhancement. Judges whether each pixel is closer to the local neighborhood's maximum or minimum, and replaces it with whichever is closer -- pixels with intermediate values get pushed toward the two extremes, raising the apparent contrast (sometimes producing an effect close to binarization).

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

HALCON に直接対応するものは無い。実装は `filters.rank.enhance_contrast(_u8s(v), disk(1+int(a*3)))` を 255 で割ったもの —— a は円盤半径を 1〜4 に振る。b は未使用。

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.

sk_enhance_contrast 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_autolevel


*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.