Pith Number

pith:77XCDBWW

pith:2025:77XCDBWWPEZIVVDR6RQGSPRIGL

not attested not anchored not stored refs resolved

Graph Signal Denoising Using Regularization by Denoising and Its Parameter Estimation

Hayate Kojima, Hiroshi Higashi, Yuichi Tanaka

Regularization by denoising adapts to graph signals when common denoisers meet its conditions, improving mean squared error over prior methods.

arxiv:2512.14213 v2 · 2025-12-16 · eess.SP

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\usepackage{pith}
\pithnumber{77XCDBWWPEZIVVDR6RQGSPRIGL}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Denoising experiments for synthetic and real-world datasets show that our proposed method improves signal denoising accuracy in mean squared error compared to existing graph signal denoising methods.

C2weakest assumption

That many graph signal denoisers, including graph neural networks, theoretically or practically satisfy the conditions required for the RED framework to apply.

C3one line summary

RED is adapted to graph signals with deep unrolling for parameter estimation, yielding lower MSE than prior graph denoising methods on synthetic and real data.

References

39 extracted · 39 resolved · 2 Pith anchors

[1] Interpretable Graph Signal Denoising Using Regularization by Denoising 2024 · doi:10.23919/eusipco63174.2024.10715194

[2] The mnist database of handwritten digit images for machine learning research [best of the web].IEEE Signal Processing Magazine, 29(6):141–142 2012 · doi:10.1109/msp.2012

[3] Graph Signal Processing: Overview, Challenges, and Applications 2018 · doi:10.1109/jproc.2018

[4] Sampling Signals on Graphs: From Theory to Applications 2020 · doi:10.1109/msp.2020.3016908

[5] A Graph Signal Processing Per- spective on Functional Brain Imaging 2018

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-27T01:04:53.811641Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ffee2186d679328ad471f460693e2832eb76092865bc828f5e36570113c8622e

Aliases

arxiv: 2512.14213 · arxiv_version: 2512.14213v2 · doi: 10.48550/arxiv.2512.14213 · pith_short_12: 77XCDBWWPEZI · pith_short_16: 77XCDBWWPEZIVVDR · pith_short_8: 77XCDBWW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/77XCDBWWPEZIVVDR6RQGSPRIGL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ffee2186d679328ad471f460693e2832eb76092865bc828f5e36570113c8622e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "27c175e2714c190b8d38cbe0565619af1bf0b85481b1bc13a36bbb410431b239",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2025-12-16T09:10:13Z",
    "title_canon_sha256": "a08314206515f196a6bd9002b14200e472c7bf2771f247893562ccd86f547a51"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.14213",
    "kind": "arxiv",
    "version": 2
  }
}