restoration op• Data kinds: voxel × voxel → voxel
• Call: import fullseye as fs; fs.ledger.vol_richardson_lucy(vol, psf, iterations=10, clip_tiny=1e-12) (to call the implementation directly, import volrestore; volrestore.vol_richardson_lucy(vol, psf, iterations=10, clip_tiny=1e-12); from the registry, ops3d.get("vol_richardson_lucy"))
Richardson–Lucy deconvolution of a non-negative volume by a known PSF.
*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).
• 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.
• deconv_fft_restore — py -3.11 examples_3d/deconv_fft_restore.py
voxel as input)voxel_to_mips · voxel_to_mesh · signed_distance_field · to_points · sobel3d · hessian3d · curvature_maps · edt_jfa
restoration)*Provenance: volrestore.py — 3D 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.