restoration op• 데이터 종류: voxel × voxel → voxel
• 호출: import fullseye as fs; fs.ledger.vol_richardson_lucy(vol, psf, iterations=10, clip_tiny=1e-12)(구현을 직접 호출하려면 import volrestore; volrestore.vol_richardson_lucy(vol, psf, iterations=10, clip_tiny=1e-12), 원장에서 가져오려면 ops3d.get("vol_richardson_lucy"))
알려진 PSF 로 음이 아닌 볼륨의 Richardson–Lucy 디컨볼루션.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
*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).
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• deconv_fft_restore — py -3.11 examples_3d/deconv_fft_restore.py
voxel 를 입력으로 받는 것)voxel_to_mips · voxel_to_mesh · signed_distance_field · to_points · sobel3d · hessian3d · curvature_maps · edt_jfa
restoration)*Provenance: volrestore.py — 3D 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.