Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions. However, these models require all the possible degradation types to be defined during the training stage while showing limited generalization to unseen degradations, which limits their practical application in complex cases. In this paper, we propose a simple but effective adaptive blind all-in-one restoration (ABAIR) model, which can address multiple degradations, generalizes well to unseen degradations, and efficiently incorporate new degradations by training a small fraction of parameters. First, we train our baseline model on a large dataset of natural images with multiple synthetic degradations, augmented with a segmentation head to estimate per-pixel degradation types, resulting in a powerful backbone able to generalize to a wide range of degradations. Second, we adapt our baseline model to varying image restoration tasks using independent low-rank adapters. Third, we learn to adaptively combine adapters to versatile images via a flexible and lightweight degradation estimator. Our model is both powerful in handling specific distortions and flexible in adapting to complex tasks, it not only outperforms the state-of-the-art by a large margin on five- and three-task IR setups, but also shows improved generalization to unseen degradations and also composite distortions.

本研究解决了现有盲全能图像恢复模型在处理未知退化类型时的局限性，提出了一种自适应盲全能恢复模型（ABAIR），能够有效处理多种退化并对未见过的退化具有良好的泛化能力。通过在大型自然图像数据集上进行训练以及灵活的适配器组合，该模型在图像恢复任务中表现出显著优越性，显示出对复合退化的适应能力。

自适应盲全能图像恢复