xmh_haar — 2D `transform` op

• Data kinds: image → image

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

Usage

> This operator's description has not been translated yet. The original text follows as it is.

Haar ウェーブレット変換(`mahotas.haar`)。画像を偶数サイズに切り詰めてから変換し、結果の係数配列(近似/水平/垂直/対角の各サブバンドが元画像と同じ大きさの配列に詰め込まれた、Matlab 風の in-place レイアウト)をそのまま min-max 正規化して画像として見せる。

分解レベルは 1 段固定。`a, b` は未使用。値そのものは復元可能な画像ではなく係数の可視化であることに注意。

Detailed usage guide

• gallery2d_geometry 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_geometry — py -3.11 examples/gallery2d_geometry.py

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

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

Same category (`transform`)

xmh_daubechies · tf_radon_sinogram

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

xmh_haar — 2D transform op