artifact op• Data kinds: sinogram → sinogram
• Call: import fullseye as fs; fs.ledger.ring_artifact_remove(sinogram, window=5, mode='median') (implementation को सीधे call करना हो तो import tomography; tomography.ring_artifact_remove(sinogram, window=5, mode='median'), registry से लेना हो तो opstomography.get("ring_artifact_remove"))
angle-averaged profile को समतल करके हर detector bin के offset हटाता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• ct_reconstruction — py -3.11 examples/ct_reconstruction.py
sinogram को input के रूप में लेते हैं)backproject_sinogram · filtered_backprojection · sart_reconstruct · beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · metal_trace_interpolate · sinogram_center_of_rotation
artifact)beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · metal_trace_interpolate
*Provenance: tomography.py — TOMOGRAPHY operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.