All-in-One Image Restoration
Overview
All-in-one image restoration studies how one model can recover images affected by different problems—such as rain, haze, blur, noise, or combinations of them—without needing a separate system for each. Common approaches use degradation-aware prompts or physical priors, specialized experts with routing, and shared CNN or transformer backbones; diffusion and other generative models are also used to reconstruct missing detail. Researchers test these systems on standard task benchmarks and increasingly on real-world, mixed-degradation, and unseen conditions, sometimes measuring downstream performance as well as image quality. A central challenge is balancing specialization with generalization: models must remove artifacts without erasing meaningful content or inventing details, while remaining efficient and controllable.
Latest papers
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