restoration op• Data kinds: image → image
• Call: fullseye.apply(img, "xsk2_wiener", 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).*
Wiener deconvolution (deblurring using a known point spread function). Calls `skimage.restoration.wiener`.
> The detailed description below is the original text — the summary and the headings are translated.
PSF はガウシアン形状を仮定して自前生成し、a が PSF の広がり
(標準偏差 `0.5 + 1.5*a`、5x5 窓)を振る。b がバランス項
(`0.05 + 0.5*b`。ノイズ対信号比の逆数に相当し、大きいほどノイズ
抑制寄りになる)を振る。**入力画像のボケが実際にこの想定ガウシアン
PSF と一致している場合にのみ有効**で、PSF の形が違うとリンギングや
復元失敗が出る。
• 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.
xsk2_wiener 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
restoration)xsk_inpaint · xsk_richardson_lucy · xsk_unwrap_phase · xcv_inpaint · xcv3_inpaint_ns · iv_richardson_lucy · iv_wiener_deconv_spatial · 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.