edges op• 資料種類:image → image
• 呼叫:fullseye.apply(img, "f2_shock_diffuse", a=0.5, b=0.5)(2-D 的模型是一張圖 + 兩個純量旋鈕 a,b∈[0,1])
*圖為在 128×128 合成輸入上實際執行的輸出。左為輸入，右為輸出。點雲以俯視散點顯示(亮度 = z)，一維序列為折線，體資料為沿 z 的最大值投影，影片為中間影格，複數影像為振幅；無法成像的回傳值直接顯示數值。*
掃描旋鈕 a(0.1 / 0.5 / 0.9，另一旋鈕取預設):
▸ f2_shock_diffuse: knob a sweep (docs site)
掃描旋鈕 b(0.1 / 0.5 / 0.9，另一旋鈕取預設):
▸ f2_shock_diffuse: knob b sweep (docs site)
換別的影像(合成場景 / 照片 / 硬幣。上排為輸入，下排為對應輸出。旋鈕取預設):
▸ f2_shock_diffuse: other inputs (docs site)
*第 4 欄是彩色 (H,W,3) 輸入。此運算子能正確處理顏色而不跨通道(不把顏色通道當作第三個空間軸摺積)。*
正則化的衝擊濾波 —— 先擴散再衝擊，如此反覆（Alvarez–Mazorra）。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
`f2_shock` is the pure Osher-Rudin shock — *contraction only*. It takes the
sign of the Laplacian of the raw image, so every noise bump is a zero crossing
and becomes its own shock: on a blurred edge with sigma 0.02 noise it raises
the flat-area noise from 0.020 to 0.050 (2.5x) and pushes the edge step to
0.539 — past the true step of 0.500, which is the noise being promoted to
structure rather than the edge being recovered.
The remedy is to alternate the two flows: expand (a Gaussian diffusion)
and contract (the shock), taking both the sign *and* the dilation/erosion
from the smoothed copy, so the contraction can only act on structure the
diffusion left standing.
`a sets the number of diffuse/shock pairs (1..10); b` sets the diffusion
width sigma (0.4..2.0) and is the trade-off knob. Measured on that same
blurred noisy edge (input: step 0.112, flat noise 0.0201):
b = 0.00 (sigma 0.40) step 0.407 noise 0.0245 (x1.2)
b = 0.10 (sigma 0.56) step 0.272 noise 0.0091 (x0.45 - noise falls)
b = 0.25 (sigma 0.80) step 0.218 noise 0.0054
b = 0.50 (sigma 1.20) step 0.187 noise 0.0032
b = 1.00 (sigma 2.00) step 0.099 noise 0.0016
So `b` near 0 keeps most of the sharpening with almost none of the noise
amplification; from `b` about 0.1 upward the filter removes noise while
still more than doubling the input's edge step.
Applicability. (1) It sharpens what is already there — it cannot recover
detail the blur destroyed, and the step never reaches the true 0.500 here.
(2) Every iteration is a morphological max/min, so thin bright lines thicken
and thin dark lines are eaten; count the iterations you can afford.
(3) With `b` large the diffusion dominates and the result approaches a plain
Gaussian blur — check that the step is still above the input's.
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者・年份)與用途見上面的族使用指南。
下面的程式已確認可以執行(與圖相同的輸入)。在 Studio 說明中，此區塊會變成按鈕，可當場載入並執行。
f2_shock_diffuse 0.40 0.50
▸ Load this pipeline · Load & run
• gallery2d_edges — py -3.11 examples/gallery2d_edges.py
image 作為輸入)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
edges)sobel_mag · prewitt_mag · roberts_mag · dog · edge_transition_width · grad_dir · log · corner_response
*Provenance: ops.py — 2D 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.