All-in-One Image Restoration (AiOIR) faces the fundamental challenge in reconciling conflicting optimization objectives across heterogeneous degradations. Existing methods are often constrained by coarse-grained control mechanisms or fixed mapping schedules, yielding suboptimal adaptation. To address this, we propose an Uncertainty-Aware Diffusion Bridge Model (UDBM), which innovatively reformulates AiOIR as a stochastic transport problem steered by pixel-wise uncertainty. By introducing a relaxed diffusion bridge formulation which replaces the strict terminal constraint with a relaxed constraint, we model the uncertainty of degradations while theoretically resolving the drift singularity inherent in standard diffusion bridges. Furthermore, we devise a dual modulation strategy: the noise schedule aligns diverse degradations into a shared high-entropy latent space, while the path schedule adaptively regulates the transport trajectory motivated by the viscous dynamics of entropy regularization. By effectively rectifying the transport geometry and dynamics, UDBM achieves state-of-the-art performance across diverse restoration tasks within a single inference step.

翻译：一体化图像复原面临的核心挑战在于协调异构退化间相互冲突的优化目标。现有方法常受限于粗粒度控制机制或固定映射方案，导致适应性欠佳。为此，我们提出一种不确定性感知扩散桥模型，其创新性地将一体化图像复原重构为受像素级不确定性引导的随机传输问题。通过引入松弛扩散桥表述——以松弛约束替代严格终端约束——我们在对退化不确定性建模的同时，从理论上解决了标准扩散桥固有的漂移奇异性。进一步，我们设计了双重调制策略：噪声调度将多样化退化对齐至共享的高熵潜空间，而路径调度则基于熵正则化的粘性动力学原理自适应调节传输轨迹。通过有效校正传输几何与动力学特性，该模型在单步推理中实现了跨多种复原任务的先进性能。