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DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration — arXiv:2608.20141

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.20141
License (as archived): CC0-1.0
Archived: 2026-10-18T12:04:48.053Z
SHA-256 of the archived text: 8dac90517f41af7dc6bdabbc5ff7f8e3d7d10cff6f0562513cda31325872ace9
Stable URL of this page: https://metraipolis.com/documento/dc_205366d987fe

How to cite

DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration — arXiv:2608.20141 — arXiv (en), https://arxiv.org/abs/2608.20141 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_205366d987fe (Metraipolis archive dc_205366d987fe, sha256 8dac90517f41af7dc6bdabbc5ff7f8e3d7d10cff6f0562513cda31325872ace9)

BibTeX

@misc{dc205366d987fe,
  title = {DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration — arXiv:2608.20141},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.20141},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_205366d987fe; sha256 8dac90517f41af7dc6bdabbc5ff7f8e3d7d10cff6f0562513cda31325872ace9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.20141. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling and lack low-level visual priors during reconstruction, leading to structural distortions and semantic inconsistencies. To address these issues, we propose a novel Dual-Prior Collaborative Network (DPC-Net), which achieves high-quality restoration by jointly exploiting degradation-semantic coupled priors and low-level visual priors. Specifically, degraded images are fed into a Degradation-Aware Network (DAN) to extract degradation-semantic coupled features. To this end, a Vision-Language Model (VLM) supervises DAN by constraining its features distribution, introducing image semantics into the encoding of degradation patterns. A Degradation-Semantic Modulation Module (DSMM) further translates this guidance into degradation-semantic coupling and propagates coupled representations to the decoder. During decoding, knowledge bases provide low-level visual priors, and the Dual-Prior Collaborative Reconstruction Module (DPCR) integrates dual-prior information to guide degradation removal while preserving structure and semantics, producing high-fidelity restored images. Extensive experiments on multiple restoration benchmarks demonstrate that DPC-Net achieves superior performance against state-of-the-art AiOIR methods.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-21 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.20141 — 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 2026-10-18T12:01:57.740Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).