Learning Restoration Actions from Degradation States for Multi-Weather Image Restoration

Multi-weather image restoration aims to recover clean images from diverse weather degradations within a unified model. Existing degradation-aware methods mainly infer degradation representations and use them to condition restoration features. However, knowing what degradation is present does not fully determine how it should be restored, as images with similar degradations may require different restoration operations depending on degradation severity, spatial distribution, and image content. To bridge this gap, we propose D2AIR, an efficient framework that learns input-adaptive restoration actions from degradation states. Its core Degradation-to-Action (D2A) module consists of a Degradation State Interpreter (DSI) and a Restoration Action Executor (RAE). DSI learns degradation-discriminative channel responses to infer degradation states and adapts latent features through degradation-aware modulation. RAE further translates the inferred states into input-adaptive restoration actions through a shared operator bank. Specifically, degradation-conditioned routing provides a degradation-dependent prior over restoration operators, while instance-conditioned routing adapts their selection to the current input, enabling flexible operator composition across degradation instances. Designed to operate at the latent bottleneck, D2A can be readily integrated into diverse restoration backbones. Experiments on single-weather, composite-degradation, and real-world benchmarks demonstrate the effectiveness of the proposed method. The code will be publicly available.