Disentanglement-wise Image Dehazing through Cross-Domain Manifold Consensus

Current dehazing methods face two intertwined challenges: (1): the misidentification of haze-related features due to domain-specific interference in both single-domain and empirically integrated multi-domain approaches, and (2): severe chromatic distortion caused by haze-induced inherent color entanglement. To overcome these limitations, we propose a unified framework centered on a $\textbf{Cross-domain Invariant Manifold}$ (CIM), which constructs a consistent latent representation space by aligning multi-domain features through shared scattering semantics. The manifold is optimized via $\textbf{consensus density-driven contrastive learning}$, effectively enhancing cross-domain consistency while eliminating domain-specific biases. Building upon this structured foundation, we further introduce a disentanglement-wise architecture, i.e.the $\textbf{Physics-Guided HSV Decomposition Network}$, that explicitly separates entangled color components to ensure robust color fidelity. Comprehensive experiments demonstrate that our CIM-D framework achieves state-of-the-art performance, effectively eliminating haze-induced color shifts and restoring natural scene appearance. The code will be made publicly available.