Disentanglement-wise Image Dehazing through Cross-Domain Manifold Consensus

Abstract

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 color entanglement. To overcome these limitations, we propose a unified framework centered on a Cross-domain Invariant Manifold (CIM), which aligns multi-domain features into a unified latent space through shared scattering semantics. The manifold is optimized via 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., 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.