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UniRestorer++: Scalable Universal Image Restoration with Multi-Granularity Degradation Modeling — arXiv:2611.06042

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.06042
License (as archived): CC0-1.0
Archived: 2026-11-05T18:29:52.825Z
SHA-256 of the archived text: d1f449b373a354921e641475013473de25ae5967d943a2f66e00530369496b93
Stable URL of this page: https://metraipolis.com/documento/dc_d8a09ffc64c8

How to cite

UniRestorer++: Scalable Universal Image Restoration with Multi-Granularity Degradation Modeling — arXiv:2611.06042 — arXiv (en), https://arxiv.org/abs/2611.06042 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d8a09ffc64c8 (Metraipolis archive dc_d8a09ffc64c8, sha256 d1f449b373a354921e641475013473de25ae5967d943a2f66e00530369496b93)

BibTeX

@misc{dcd8a09ffc64c8,
  title = {UniRestorer++: Scalable Universal Image Restoration with Multi-Granularity Degradation Modeling — arXiv:2611.06042},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.06042},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d8a09ffc64c8; sha256 d1f449b373a354921e641475013473de25ae5967d943a2f66e00530369496b93},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.06042. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Universal image restoration aims to handle diverse degradations within a single model, but a reliable scaling strategy remains underexplored. Increasing backbone depth and width can lead to performance saturation, while degradation-aware specialization depends on accurate degradation estimation and is vulnerable to errors on out-of-distribution and real-world inputs. We propose UniRestorer++, a scalable framework based on multi-granularity degradation modeling. It progressively refines degradation groups and introduces group-specific experts, coupling capacity growth with specialization. Degradation-estimation-based routing selects a root-to-leaf path, while granularity-estimation-based routing chooses a level along that path to balance specialization and robustness. Spatially dense degradation representation maps further enable region-aware routing for spatially non-uniform corruptions. We develop CDD-50, a benchmark covering 50 degradation categories from single to penta-degradation. Experiments show approximately log-linear PSNR trends over the evaluated model-size range and stronger scaling gains on out-of-distribution and real-world data than vanilla MoE scaling. UniRestorer++ achieves state-of-the-art performance across benchmarks and demonstrates zero-shot generalization and few-shot transfer on real-world images. Code: this https URL

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.06042 — 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 2026-11-05T18:19:49.048Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).