xcv_detail_enhance — 2D `gray` op

• Data kinds: image → image

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

Usage

OpenCV's detailEnhance (detail enhancement).

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

エッジ保存平滑化をベースに、平坦部は滑らかに保ったまま微細な

ディテール(テクスチャ)のコントラストを持ち上げる。

`a が空間範囲 sigma_s を 10〜50 で振る。b` が色差許容

`sigma_r を 0.1〜0.4 で振る。xcv_edge_preserving` と同系の

処理だが、こちらは細部を「消す」のではなく「強調する」方向のフィルタ。

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.

Runnable examples (verified samples that actually call this op)

• gallery2d_gray_arith — py -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).*

xcv_detail_enhance — 2D gray op