fullseye

ring_artifact_remove — TOMOGRAPHY artifact op

• データ種: sinogram → sinogram
• 呼び出し: import fullseye as fs; fs.ledger.ring_artifact_remove(sinogram, window=5, mode='median') (実装を直接呼ぶなら import tomography; tomography.ring_artifact_remove(sinogram, window=5, mode='median')、台帳から引くなら opstomography.get("ring_artifact_remove"))

使い方

Remove per-detector-bin offsets by flattening the angle-averaged profile.

The mean of a sinogram column over all angles is a smooth function of the detector position for any object that stays inside the field of view — it is essentially the object’s mass seen from every side. A gain error adds a constant to one column, so it appears in that mean as a spike on a smooth curve. Smoothing the mean profile and subtracting the difference removes the spike and leaves the object.

Measured on the Shepp-Logan phantom scaled to a peak line integral of 1.18 (i.e. CT-realistic, see the note below) with gain_sigma=0.02: the reconstruction’s normalised RMS error against the truth goes 0.0250 (clean) -> 0.0643 (with rings) -> 0.0358 (removed at the default window), so 72 % of the damage is undone.

The window is the whole argument, and it was chosen by measurement rather than by taste. Removed fraction, against the damage the same call does to an already-clean sinogram:

window   median: undone / damage    mean: undone / damage
   3       61.0 % / +0.0000           70.4 % / +0.0004
   5       72.3 % / +0.0002           82.6 % / +0.0019
   7       74.3 % / +0.0017           82.4 % / +0.0042
  11       73.6 % / +0.0025           74.0 % / +0.0091
  31       73.3 % / +0.0043           37.4 % / +0.0244
  61       58.2 % / +0.0109            9.0 % / +0.0356

The default is window=5, mode="median" because it is the setting that removes most of the rings while doing almost nothing to a sinogram that did not need it — and that is the property that matters, because this operator will be run on scans whose rings nobody has measured. mean at the same window removes 10 points more and costs 10x the collateral damage; wide windows are worse at both.

Two failure modes are stated rather than hidden. This cannot separate a real object feature that is thin in the detector direction and present at every angle — the axis of rotation itself is the extreme case — from a gain error. And gain_sigma is in line-integral units, so how much a given gain error matters depends entirely on how large the line integrals are: on the same phantom left in raw pixel units (peak line integral 70.9 rather than 1.18) the identical 2 % gain error changes the reconstruction’s error by less than 0.0001 and this operator has nothing to do. That is not a bug in either place — it is what “2 % of the signal” means when the signal is 60x larger.

:param sinogram: (n_angles, n_detectors). :param window: smoothing width in detector bins, an odd int 3 .. n_det. :param mode: "median" (robust, the default) or "mean". :returns: (n_angles, n_detectors) float64 sinogram. :raises ValueError: on an even or out-of-range window, or an unknown mode.

参考(サンプルデータ・文献)

• 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。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• ct_reconstruction — py -3.11 examples/ct_reconstruction.py

型が繋がる次の op(sinogram を入力に取れる)

backproject_sinogram · filtered_backprojection · sart_reconstruct · beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · metal_trace_interpolate · sinogram_center_of_rotation

同カテゴリ(artifact)

beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · metal_trace_interpolate

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Provenance: tomography.py — TOMOGRAPHY 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.

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