artifact op• 데이터 종류: sinogram → sinogram
• 호출: import tomography; tomography.ring_artifact_apply(sinogram, gain_sigma=0.02, seed=0, offsets=None)(또는 opstomography.get("ring_artifact_apply"))
검출기에 빈별 게인 오차를 줍니다 —— 링 아티팩트의 원천.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
A detector bin whose gain is `g reports I = g I_true`, so after the
logarithm the line integral picks up a constant offset `-ln g` at that
bin, the same at every angle. Back-projecting a constant column smears it
around the rotation axis, and the reconstruction grows a ring at the radius
that bin's rays are tangent to. One bad pixel, one perfect circle.
The offsets are drawn once from `N(0, gain_sigma)` with a fixed *seed* and
applied to every row, because the whole point is that the error does not
vary with angle — that is what distinguishes a ring from noise, and what makes
:func:ring_artifact_remove possible.
:param sinogram: `(n_angles, n_detectors)`.
:param gain_sigma: standard deviation of the per-bin offset, `>= 0`.
:param seed: RNG seed; there is no `None` (determinism is a contract here).
:param offsets: explicit `(n_detectors,)` offsets; overrides the random draw.
:returns: `(n_angles, n_detectors)` float64 sinogram.
:raises ValueError: on a negative sigma, a non-int seed, or an *offsets* whose
length is not the detector count.
• 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.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• ct_reconstruction — py -3.11 examples/ct_reconstruction.py
sinogram 를 입력으로 받는 것)backproject_sinogram · filtered_backprojection · sart_reconstruct · beam_hardening_apply · beam_hardening_correct · ring_artifact_remove · metal_trace_interpolate · sinogram_center_of_rotation
artifact)beam_hardening_apply · beam_hardening_correct · ring_artifact_remove · metal_trace_interpolate
*Provenance: tomography.py — TOMOGRAPHY 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.