UniLens: Degradation-Centric Learning and Evaluation for Real-World All-in-One Image Restoration

All-in-one image restoration aims to handle multiple restoration tasks with a single model, but its generalization to real images is still difficult. A major obstacle is deciding which training examples to add, since simply scaling up dataset size does not guarantee coverage of real-world degradation patterns. In this paper, we propose UniLens, a unified pipeline that connects degradation-aware data construction, model adaptation, and task-conditioned evaluation for real-world all-in-one restoration. First, we formulate data construction as a degradation-coverage problem: how can we represent degradations in real-world images and use this representation to build training pairs? The key is to learn a degradation space that is less sensitive to scene content and reveals which real-world degradation patterns the training data miss. We then use this space to guide data synthesis that adds training pairs to cover these patterns. Second, building on this broader coverage, we augment paired supervision with instruction-dependent targets for single, joint, and selective removal, providing richer compositional supervision for all-in-one restoration under real-world composite degradations. An instruction-conditioned mixture of LoRA experts further supports these transformations, with a shared low-rank branch capturing common restoration knowledge and sparse experts providing request-dependent adaptation. Finally, we introduce a task-conditioned vision-language evaluator that assesses what degradation remains and how much the image has improved. Our method achieves state-of-the-art performance on real-world image restoration. Compared with FoundIR-v2, it improves task-conditioned restoration scores by 24.8% on public benchmarks and by 45.3% on real images with composite degradations.