xsk3_estimate_sigma — 2D `features` op

• Data kinds: image → feature

• Call: fullseye.apply(img, "xsk3_estimate_sigma", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

xsk3_estimate_sigma: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

Usage

Noise standard deviation estimation (skimage `restoration.estimate_sigma`, Donoho's estimator based on the median absolute deviation (MAD) of wavelet coefficients). Returns the estimate multiplied by 5 and capped at 1.0 as a feature.

> The detailed description below is the original text — the summary and the headings are translated.

`a, b` は未使用。デノイズ強度(例えば閾値処理のパラメータ)を画像ごとに自動決定したい場合の目安に使える。

Detailed usage guide

• gallery2d_features family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

xsk3_estimate_sigma 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_features — py -3.11 examples/gallery2d_features.py

Ops the type connects to (they accept `feature` as input)

identity

Same category (`features`)

blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_entropy_feat · sk_blur_effect

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

xsk3_estimate_sigma — 2D features op