Symmetry-Constrained Dual-Path Physics-Guided Mamba Network: Balancing Performance and Efficiency in Underwater Image Enhancement

The field of underwater image enhancement (UIE) has advanced significantly, yet it continues to grapple with persistent challenges stemming from complex, spatially varying optical degradations such as light absorption, scattering, and color distortion. These factors often impede the efficient deployment of enhancement models. Conventional approaches frequently rely on uniform processing strategies that neither adapt effectively to diverse degradation patterns nor adequately incorporate physical principles, resulting in a trade-off between enhancement quality and computational efficiency. To overcome these limitations, we propose a Dual-Path Physics-Guided Mamba Network (DPPGM), a lightweight framework designed to synergize physical optics modeling with data-driven learning. Extensive experiments on three benchmark datasets (UIEB, LSUI, and U45) demonstrate that DPPGM outperforms 13 state-of-the-art methods, achieving an exceptional balance with only 1.48 M parameters and 25.39 G FLOPs. The key to this performance is a symmetry-constrained architecture: it incorporates a dual-path Mamba module for degradation-aware processing, physics-guided optimization based on the Jaffe–McGlamery model, and compact subspace fusion, ensuring that quality and efficiency are mutually reinforced rather than competing objectives.