In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the effectiveness of our DIL on the generalization capability for unseen distortion types and degrees. Our code will be available at https://github.com/lixinustc/Causal-IR-DIL.

翻译：近年来，深度神经网络（DNNs）在图像复原领域取得了显著进展。然而，一个关键局限在于它们无法很好地泛化到具有不同程度或类型的真实世界退化场景。本文首次从因果视角提出一种新颖的图像复原训练策略，旨在提升DNNs对未知退化的泛化能力。我们将该方法命名为失真不变表示学习（Distortion Invariant representation Learning, DIL），其核心思想是将每种失真类型和程度视为特定混杂因子，通过消除每种退化带来的有害混杂效应来学习失真不变表示。我们基于因果性中的后门准则，通过从优化角度建模不同失真的干预效应推导出DIL。具体而言，我们引入反事实失真增强技术，模拟虚拟失真类型和程度作为混杂因子；随后，通过基于对应失真图像的虚拟模型更新来实例化每种失真的干预效应，并从元学习视角消除这些效应。大量实验证明了DIL在未知失真类型和程度泛化能力上的有效性。我们的代码将发布于https://github.com/lixinustc/Causal-IR-DIL。