restoration op• データ種: voxel × voxel → voxel
• 呼び出し: import fullseye as fs; fs.ledger.vol_richardson_lucy(vol, psf, iterations=10, clip_tiny=1e-12) (実装を直接呼ぶなら import volrestore; volrestore.vol_richardson_lucy(vol, psf, iterations=10, clip_tiny=1e-12)、台帳から引くなら ops3d.get("vol_richardson_lucy"))
既知 PSF による非負ボリュームの Richardson–Lucy デコンボリューション。
> 以下の詳細説明は原文のままです —— 要約と見出しは訳出済み。
*psf* is a 3-D non-negative kernel (any odd/even size smaller than the
volume; it is normalised to sum 1 internally so overall intensity is
preserved). *iterations* trades sharpness against noise amplification —
5-30 is the practical range (see the module notes on semi-convergence).
Negative voxels are refused (RL is a Poisson model) — except *rounding
dust*: values no lower than `-NEGATIVE_DUST_TOL * max|vol|` (1e-9
relative; an FFT-blurred observation typically carries -1e-16) are clipped
to 0 instead of rejected, so the module's own forward model feeds back in.
Returns the deblurred `(D, H, W)` float64 volume (non-negative).
Measured on the test scene (binary sphere pair blurred by a sigma-2
Gaussian): the RMSE to ground truth falls to 0.81x the blurred
observation's at 10 iterations and 0.68x at 50 — genuine but *gradual*,
because the residual is dominated by the spheres' hard edges, which RL
recovers slowly. What converges fast is the *forward consistency*:
re-blurring the estimate reproduces the observation almost exactly (that
is the quantity the RL update actually optimises).
• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• deconv_fft_restore — py -3.11 examples_3d/deconv_fft_restore.py
voxel を入力に取れる)voxel_to_mips · voxel_to_mesh · signed_distance_field · to_points · sobel3d · hessian3d · curvature_maps · edt_jfa
restoration)*Provenance: volrestore.py — 3D operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.