pith:CXAD23ZW
FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising
Frequency-domain anchoring and perturbation generate pseudo-labels that train a denoiser on noisy low-dose CT data alone.
arxiv:2605.10583 v2 · 2026-05-11 · cs.CV
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\pithnumber{CXAD23ZW6D4LREKD7MUNJCSCYM}
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Claims
this work proposes FrequencyCT, the first zero-shot self-supervised method for pseudo-label generation in the frequency domain for low-dose CT denoising. [...] Evaluation results on multiple public and real-world datasets confirm the clinical application potential of this research, which will have a revolutionary impact on the field of denoising.
Leveraging the characteristic of the frequency domain that largely isolates noise from clean signals, a regional low-frequency anchoring technique is proposed.
FrequencyCT generates pseudo-labels via regional low-frequency anchoring, phase-preserving modulation, and high-frequency mask perturbation for self-supervised low-dose CT denoising.
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Receipt and verification
| First computed | 2026-05-28T01:04:42.141554Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
15c03d6f36f0f8b89143fb28d48a42c3381e0be4981e8774f63a9b4de56f7a5f
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/CXAD23ZW6D4LREKD7MUNJCSCYM \
| 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: 15c03d6f36f0f8b89143fb28d48a42c3381e0be4981e8774f63a9b4de56f7a5f
Canonical record JSON
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