xmh_daubechies — 2D `transform` op

• Data kinds: image → image

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

Usage

Daubechies wavelet transform (`mahotas.daubechies`). Same layout as xmh_haar (single level, coefficients packed into an array of the original image size and min-max normalized), but with the basis changed to the Daubechies family.

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

`a はウェーブレットの種類を D2/D4/D6/D8 の 4 択で切り替える(タップ数が増えるほど滑らかで長いフィルタになる)。b` は未使用。

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_haar · 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_daubechies — 2D transform op