tb_quat_norm — 2D `typed` op

• Data kinds: qimage → image

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

*No figure: this op takes qimage as input. A Studio program starting from an image cannot reach that type — see the runnable examples below for how it is used.*

Usage

Per-pixel quaternion modulus `|q| = sqrt(w^2+x^2+y^2+z^2)`. → (H, W).

Raw / unnormalised, following `complexops.cx_magnitude`: a modulus is a

metric quantity and routinely exceeds one (a QFT spectrum's DC term is huge).

For a colour quaternion it is the colour *magnitude* — the length of the RGB

vector, i.e. luminance in the L2 sense; for a monogenic signal it is the

local amplitude and :func:monogenic_amplitude is the name that says so.

Use `imgio.normalize` for a displayable view.

Typed bridge of the quat op `quat_norm into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b` are unused.

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.

Runnable examples (verified samples that actually call this op)

• (none yet)

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

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

Same category (`typed`)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal

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

tb_quat_norm — 2D typed op