  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.
