All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing prompt information or leveraging large models, the added learning modules increase system complexity and hinder real-time applicability. In this paper, we adopt a physical degradation modeling perspective and predict a task-aware inverse degradation operator for efficient all-in-one image restoration. The framework consists of two stages. In the first stage, the predicted inverse operator produces an initial restored image together with an uncertainty perception map that highlights regions difficult to reconstruct, ensuring restoration reliability. In the second stage, the restoration is further refined under the guidance of this uncertainty map. The same inverse operator prediction network is used in both stages, with task-aware parameters introduced after operator prediction to adapt to different degradation tasks. Moreover, by accelerating the convolution of the inverse operator, the proposed method achieves efficient all-in-one image restoration. The resulting tightly integrated architecture, termed OPIR, is extensively validated through experiments, demonstrating superior all-in-one restoration performance while remaining highly competitive on task-aligned restoration.

翻译：一体化图像复原旨在通过单一训练模型自适应地处理多种复原任务。尽管现有方法通过引入提示信息或利用大型模型取得了良好效果，但新增的学习模块增加了系统复杂性并阻碍了实时应用。本文从物理退化建模的视角出发，通过预测任务感知的逆退化算子来实现高效的一体化图像复原。该框架包含两个阶段：第一阶段中，预测的逆算子生成初始复原图像及不确定性感知图，后者突出标注难以重建的区域以确保复原可靠性；第二阶段在此不确定性图的引导下进一步优化复原结果。两个阶段使用相同的逆算子预测网络，并在算子预测后引入任务感知参数以适应不同退化任务。此外，通过加速逆算子的卷积运算，本方法实现了高效的一体化图像复原。最终构建的紧密集成架构（称为OPIR）经过大量实验验证，在保持任务对齐复原竞争力的同时，展现出卓越的一体化复原性能。