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])

xcv_detail_enhance: 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

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.

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.

xcv_detail_enhance 0.50 0.50

▸ Load this pipeline · Load & run

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