alife_perona_malik — 2D `artificial-life` op

• Data kinds: image → image

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

Usage

Perona-Malik anisotropic (edge-preserving) diffusion.

Explicit 4-neighbour update I <- I + lam * sum_dir g(|grad_dir I|) grad_dir I

with the Perona-Malik conductance g(s) = 1/(1 + (s/kappa)^2): inside a flat

region g -> 1 and the field diffuses like the heat equation, across a strong

edge g -> 0 and the edge is preserved. Unlike a bilateral filter the

conductance is recomputed from the *evolving* field at every iteration, which

is what makes the edges sharpen rather than merely survive. `a` sets the

edge scale kappa = 0.02 + 0.2a, `b` the iteration count 1 + int(15b);

lambda is fixed at 0.2 (<= 0.25, the explicit-scheme stability bound).

Detailed usage guide

• gallery2d_physics_alife_3d 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_physics_alife_3d — py -3.11 examples/gallery2d_physics_alife_3d.py

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

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

Same category (`artificial-life`)

alife_gray_scott · alife_turing · alife_life_step · alife_cyclic_ca · alife_curvature_flow · alife_dla · alife_reaction_bz · alife_wolfram1d

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

alife_perona_malik — 2D artificial-life op