  Reconstructing missing details from degraded low-quality inputs poses a
significant challenge. Recent progress in image restoration has demonstrated
the efficacy of learning large models capable of addressing various
degradations simultaneously. Nonetheless, these approaches introduce
considerable computational overhead and complex learning paradigms, limiting
their practical utility. In response, we propose \textit{DaAIR}, an efficient
All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the
low-rank regime to collaboratively mine shared aspects and subtle nuances
across diverse degradations, generating a degradation-aware embedding. By
dynamically allocating model capacity to input degradations, we realize an
efficient restorer integrating holistic and specific learning within a unified
model. Furthermore, DaAIR introduces a cost-efficient parameter update
mechanism that enhances degradation awareness while maintaining computational
efficiency. Extensive comparisons across five image degradations demonstrate
that our DaAIR outperforms both state-of-the-art All-in-One models and
degradation-specific counterparts, affirming our efficacy and practicality. The
source will be publicly made available at https://eduardzamfir.github.io/daair/
