restoration op• Data kinds: image → image
• Call: fullseye.apply(img, "iv_gradient_inpaint", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
Harmonic (Laplace) inpainting of a central masked window. The interior of a centred window (side fraction from `a) is discarded and refilled by solving nabla^2 u = 0 with Dirichlet boundary equal to the surrounding known pixels (Jacobi relaxation = repeatedly replacing each masked pixel by its 4-neighbour mean). The recovered interior is the smoothest (minimum-gradient) fill of the hole. a sets the window size; b` is ignored.
• gallery2d_smoothing_rank family guide
• 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.
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
restoration)xsk_inpaint · xsk_richardson_lucy · xsk_unwrap_phase · xcv_inpaint · xsk2_wiener · xcv3_inpaint_ns · iv_richardson_lucy · iv_wiener_deconv_spatial
*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.