English(EN) UniRestorer++: Scalable Universal Image Restoration with Multi-Granularity Degradation Modeling

UniRestorer++框架通过多粒度退化建模增强图像恢复能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了UniRestorer++，一个专为可扩展通用图像恢复设计的新型框架。该系统通过采用多粒度退化建模来解决当前模型的局限性，该建模逐步优化退化组并整合特定于组的专家。该框架利用基于退化估计和粒度估计的路径选择机制来平衡专业化和鲁棒性，并通过空间密集型退化表示进行区域感知路由，进一步增强了其性能。UniRestorer++在基准测试中取得了最先进的成果，并在分布外和真实世界图像上表现出改进的泛化能力，显示出强大的性能提升。 AI

影响通过一个可扩展且鲁棒的框架增强图像恢复能力，有可能在各种真实世界图像损坏上提高性能。

排序理由该项目是一篇研究论文，详细介绍了一个用于图像恢复的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jingbo Lin, Zhilu Zhang, Fan Li, Renjing Pei, Wenbo Li, Hang Xu, Hongzhi Zhang, Wangmeng Zuo · 2026-11-05 05:00

UniRestorer++：具有多粒度退化建模的可扩展通用图像恢复

arXiv:2611.06042v1 Announce Type: new Abstract: 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-…

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报道来源 [1]

UniRestorer++：具有多粒度退化建模的可扩展通用图像恢复

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