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Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting — arXiv:2609.32844

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32844
License (as archived): CC0-1.0
Archived: 2026-10-07T23:55:04.381Z
SHA-256 of the archived text: e32ac66a7c13e66424569de967b366622eb75736b75d00955bcc1da228823182
Stable URL of this page: https://metraipolis.com/documento/dc_0bf45faaac64

How to cite

Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting — arXiv:2609.32844 — arXiv (en), https://arxiv.org/abs/2609.32844 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0bf45faaac64 (Metraipolis archive dc_0bf45faaac64, sha256 e32ac66a7c13e66424569de967b366622eb75736b75d00955bcc1da228823182)

BibTeX

@misc{dc0bf45faaac64,
  title = {Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting — arXiv:2609.32844},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32844},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0bf45faaac64; sha256 e32ac66a7c13e66424569de967b366622eb75736b75d00955bcc1da228823182},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32844. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Medical ultrasound (US) is inherently degraded by speckle, a granular interference pattern that is often treated as a complex form of noise in image restoration. However, unlike random noise, US speckle originates from coherent scattering within tissue and is therefore highly spatially dependent and deterministic under fixed acquisition conditions, making US speckle suppression fundamentally different from natural image denoising. Because speckle-free US targets are unavailable in practice, self-supervised denoising is necessary. Blind-spot networks (BSN) are the dominant self-supervised paradigm for natural images, but their pixel-wise masking strategy assumes spatially independent noise, an assumption poorly matched to US speckle, which is spatially correlated over multiple pixels rather than pixel-wise independent. To address this mismatch, we propose Mask2Restore, a self-supervised US despeckling framework that reformulates despeckling as contextual inpainting with block-wise masking on single noisy images. Unlike pixel-wise BSN masking, block-wise masking addresses this multi-pixel speckle correlation by removing locally correlated speckle neighborhoods and shifting the reconstruction cues used by the network from adjacent speckle correlations to broader anatomical context. We further introduce cross-resolution context regularization (CRCR), which suppresses residual speckle bias by enforcing consistency across multi-resolution predictions. Experiments on simulated and in vivo carotid US, unseen fine-structure cases, and downstream cardiac segmentation demonstrate improved speckle-detail trade-offs, better preservation of fine anatomical structures, and practical value for subsequent image analysis.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.32844 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T23:29:27.671Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).