iv_wiener_deconv_spatial — 2D restoration op

• Data kinds: image → image

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

Usage

Self-contained spatial Wiener deconvolution of an assumed small Gaussian PSF. `a sets the assumed blur sigma in (0, WIENER_SIGMA_MAX] to invert (larger a -> stronger sharpening); b` sets the noise-to-signal ratio nsr = 1e-3 + b*0.15 (larger b -> gentler, more regularized restoration).

Detailed usage guide

• gallery2d_smoothing_rank 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_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

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

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

Same category (restoration)

xsk_inpaint · xsk_richardson_lucy · xsk_unwrap_phase · xcv_inpaint · xsk2_wiener · xcv3_inpaint_ns · iv_richardson_lucy · iv_unsharp_deblur

*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.