Breaking the Synthetic Barrier: Towards Stable and Generalizable Real-World Image Dehazing

Existing learning-based dehazing methods perform well on synthetic data but struggle in real scenarios due to the domain gap, causing residual haze and detail loss. To address this, we propose a Multilevel Subspace Distribution Adapter (MSDA) to progressively reduce the feature distribution gap through hierarchical subspace modeling. We also introduce a Dual-Domain Synchronous Optimization (DDSO) strategy that jointly leverages synthetic supervision and adaptation to the real domain in a unified training scheme. Extensive experiments underscore the superiority of our approach and its excellence on no-reference image quality metrics.