ph_total_variation_flow — 2D physics op

• Data kinds: image → image

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

Usage

Total-variation (Rudin-Osher-Fatemi) denoising flow (no HALCON operator, "").

Gradient descent of the ROF energy TV(I) + (lam/2)||I - I0||^2:

I_t = div(grad I / |grad I|) - lam (I - I0).

The TV term (curvature of the level sets) flattens noise while preserving sharp

edges; the fidelity term keeps the result anchored to the noisy input I0 so it

denoises rather than collapsing to a constant. `a` sets the step count,

`b` the fidelity weight lam.

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 (physics)

ph_perona_malik · ph_coherence_enhancing_diffusion · ph_reaction_diffusion · ph_heat_flow · ph_mean_curvature_motion

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

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.