In the past decade, image restoration has witnessed a remarkable performance leap. However, existing methods still face two critical limitations: i) many methods train distortion-specific models, which struggle to address mixed distortions; ii) most methods employ reconstruction loss during training, with little consideration for the intended use of the restored images (e.g., human viewing, detection, or segmentation), leading to a mismatch between training objectives and application goals. In this paper, we propose a scalable framework for image restoration and enhancement built upon the reinforcement learning (RL) paradigm. At the core of our framework, the RL agent is capable of accurately perceiving distortion contexts and dynamically adjusting diverse restoration operators, which ensures our framework to effectively handle mixed-distortions, and generalize well to new tasks with minor additional cost. Furthermore, RL relaxes the differentiability constraints on optimization objectives, which enables our framework to directly optimize the restoration process toward application goals, thereby bridging the gap between training objectives and downstream demands. Extensive objective and subjective evaluations demonstrate that our method not only generates visually pleasing results for human perception but also significantly improves the performance of machine vision tasks such as detection and segmentation. We further validate the framework’s scalability through generalization evaluations on unseen distortions and multi-objective optimization experiments. The results confirm the model’s superior few-shot and zero-shot capabilities compared to existing methods, as well as its flexibility in addressing multiple competing objectives. Finally, comprehensive ablation studies and hyperparameter analyses provide valuable insights into the design choices and behavior of our framework.
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