  Relying on paired synthetic data, existing learning-based Computational
Aberration Correction (CAC) methods are confronted with the intricate and
multifaceted synthetic-to-real domain gap, which leads to suboptimal
performance in real-world applications. In this paper, in contrast to improving
the simulation pipeline, we deliver a novel insight into real-world CAC from
the perspective of Unsupervised Domain Adaptation (UDA). By incorporating
readily accessible unpaired real-world data into training, we formalize the
Domain Adaptive CAC (DACAC) task, and then introduce a comprehensive Real-world
aberrated images (Realab) dataset to benchmark it. The setup task presents a
formidable challenge due to the intricacy of understanding the target
aberration domain. To this intent, we propose a novel Quntized Domain-Mixing
Representation (QDMR) framework as a potent solution to the issue. QDMR adapts
the CAC model to the target domain from three key aspects: (1) reconstructing
aberrated images of both domains by a VQGAN to learn a Domain-Mixing Codebook
(DMC) which characterizes the degradation-aware priors; (2) modulating the deep
features in CAC model with DMC to transfer the target domain knowledge; and (3)
leveraging the trained VQGAN to generate pseudo target aberrated images from
the source ones for convincing target domain supervision. Extensive experiments
on both synthetic and real-world benchmarks reveal that the models with QDMR
consistently surpass the competitive methods in mitigating the
synthetic-to-real gap, which produces visually pleasant real-world CAC results
with fewer artifacts. Codes and datasets will be made publicly available.
