filtering op• Data kinds: image → image
• Call: fullseye.apply(img, "tf_gradient_domain_reintegrate", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
gradient_domain_reintegrate: threshold the gradient, then Poisson-reintegrate.
The forward gradient (gx, gy) is computed, gradient vectors whose magnitude is
below a threshold `t = a * max|grad|` are zeroed (small texture/noise
gradients discarded, strong edges kept), and the image is reconstructed from
the modified gradient field by solving the Poisson equation
`lap f = div(g) with an FFT solver. With a == 0` every gradient is kept
and the original image is recovered (up to a constant); with `a > 0` flat
regions are flattened while edges survive -- an edge-preserving gradient-domain
filter. `b` is unused. Output rescaled to [0,1].
• 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.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
tf_gradient_domain_reintegrate 0.50 0.50
▸ Load this pipeline · Load & run
• 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
filtering)—
*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.