fullseye

iv_richardson_lucy_until — CONVERGE stopping op

• データ種: image2d → table
• 呼び出し: import fullseye as fs; fs.ledger.iv_richardson_lucy_until(image=None, psf_sigma=1.5, psf=None, noise='poisson', sigma=None, gain=1.0, tau=1.0, max_iter=2000, plateau_window=25, plateau_rtol=0.0001, keep_history=True, callback=None) -> 'dict' (実装を直接呼ぶなら import deconvstop; deconvstop.iv_richardson_lucy_until(image=None, psf_sigma=1.5, psf=None, noise='poisson', sigma=None, gain=1.0, tau=1.0, max_iter=2000, plateau_window=25, plateau_rtol=0.0001, keep_history=True, callback=None) -> 'dict'、台帳から引くなら opsconverge.get("iv_richardson_lucy_until"))

使い方

Richardson-Lucy deconvolution stopped by the discrepancy principle (Poisson: Bertero 2010, tau = 1; Gaussian: Morozov); refuses data outside the model’s range.

image: the blurred, noisy 2-D image. psf: a 2-D PSF (normalised here), or a Gaussian of psf_sigma px. The blur is periodic (FFT), so the RL step x <- x * H^T(y / H x) keeps sum(H x) = sum(y) exactly (Lemma 3.2 of Benvenuto & Piana).

• noise="poisson": image * gain must be photon counts (non-negative integers within 1e-6); stop at the first k with the Poisson discrepancy D_k <= tau (Benvenuto & Piana 2012 eq. (30); tau = 1 as proposed by Bertero et al. 2010).
• noise="gaussian": sigma (the noise standard deviation, in image units) is required; stop at the first k with ||H x_k - y||^2 / (N sigma^2) <= tau (Morozov, eq. (18)). The image must be non-negative (add the known offset first).

The iteration starts from the flat image of the data’s mean (k = 0). Returns image (x_k in the input units), stopped, status, k_stop, discrepancy_history (D_0, D_1, …), mass_rel_error_max (Lemma 3.2), tau and noise.

Not stopped -> image = None and status:

• "outside_model_range" — the discrepancy levelled off above tau (relative decrease below plateau_rtol per iteration over plateau_window iterations): y is not in the range of the model, so the criterion can never be met (Benvenuto & Piana: the rule is a regularisation iff y is in H(C)). Typical causes: the assumed PSF is wider than the real blur, a background is missing, the noise level is wrong.
• "max_iter" — still decreasing when max_iter ran out.

The last iterate is under last_iterate (not an estimate). callback(k, x_k, D_k), if given, is called after every iteration (x_k in the input units) — for plotting the run.

詳しい使い方ガイド

• convergent_computation ファミリ ガイド

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)

• converge_tour — py -3.11 examples/converge_tour.py

型が繋がる次の op(table を入力に取れる)

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同カテゴリ(stopping)

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Provenance: deconvstop.py — CONVERGE 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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