All-in-one image restoration aims to address diverse degradation types using a single unified model. Existing methods typically rely on degradation priors to guide restoration, yet often struggle to reconstruct content in severely degraded regions. Although recent works leverage semantic information to facilitate content generation, integrating it into the shallow layers of diffusion models often disrupts spatial structures (\emph{e.g.}, blurring artifacts). To address this issue, we propose a Triple-Prior Guided Diffusion (TPGDiff) network for unified image restoration. TPGDiff incorporates degradation priors throughout the diffusion trajectory, while introducing structural priors into shallow layers and semantic priors into deep layers, enabling hierarchical and complementary prior guidance for image reconstruction. Specifically, we leverage multi-source structural cues as structural priors to capture fine-grained details and guide shallow layers representations. To complement this design, we further develop a distillation-driven semantic extractor that yields robust semantic priors, ensuring reliable high-level guidance at deep layers even under severe degradations. Furthermore, a degradation extractor is employed to learn degradation-aware priors, enabling stage-adaptive control of the diffusion process across all timesteps. Extensive experiments on both single- and multi-degradation benchmarks demonstrate that TPGDiff achieves superior performance and generalization across diverse restoration scenarios. Our project page is: https://leoyjtu.github.io/tpgdiff-project.

翻译：一体化图像恢复旨在通过单一统一模型处理多种退化类型。现有方法通常依赖退化先验指导恢复过程，但在严重退化区域的内容重建方面仍面临困难。尽管近期研究利用语义信息促进内容生成，将其融入扩散模型的浅层网络时常会破坏空间结构（例如产生模糊伪影）。为解决这一问题，我们提出用于统一图像恢复的三重先验引导扩散（TPGDiff）网络。TPGDiff在扩散轨迹全程融入退化先验，同时将结构先验引入浅层网络、语义先验引入深层网络，从而构建分层互补的先验引导图像重建框架。具体而言，我们利用多源结构线索作为结构先验来捕获细粒度细节，并指导浅层表征学习。为完善该设计，我们进一步开发了蒸馏驱动的语义提取器以生成鲁棒的语义先验，确保在严重退化条件下深层网络仍能获得可靠的高层语义指导。此外，通过部署退化提取器学习退化感知先验，实现所有时间步中扩散过程的阶段自适应控制。在单退化和多退化基准测试上的大量实验表明，TPGDiff在多种恢复场景中均实现了卓越的性能与泛化能力。项目页面详见：https://leoyjtu.github.io/tpgdiff-project。