xsp_dct_denoise — 2D `smoothing` op

• Data kinds: image → image

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

xsp_dct_denoise: 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 removal via hard-thresholding of DCT coefficients (a DCT-based version of wavelet shrinkage).

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

しきい値は最大係数に対する相対値で決める（``thr = (0.01 + 0.2*a) *

max(|C|)`）。a が大きいほど強く間引かれ、b` は未使用。しきい値

未満の DCT 係数を 0 にしてから逆変換するので、弱いテクスチャごと消え

やすい代わりに広い平坦領域のノイズはよく落ちる。

Detailed usage guide

• gallery2d_smoothing_rank 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.

xsp_dct_denoise 0.35 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`smoothing`)

gaussian · mean_box · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm

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

xsp_dct_denoise — 2D smoothing op