Feature Complementarity-Guided Multi-Weight Multi-Scale Fusion Framework for Underwater Image Enhancement

The selective wavelength absorption and scattering effects caused by complex underwater optical environments lead to a significant contradiction between color restoration and structural preservation in image enhancement. To break through this bottleneck, this paper proposes a multi-weight-guided hierarchical feature fusion framework, which transforms underwater image enhancement into a problem of optimal integration of multi-dimensional feature streams. Addressing underwater image degradation, the method constructs three complementary feature branches targeting visibility restoration, contrast enhancement, and texture compensation. Guided by multiple weights derived from Laplacian contrast, saliency, and saturation, a Laplacian and Gaussian pyramid-based multi-scale fusion strategy is designed, achieving the simultaneous preservation of global structure and enhancement of local high-frequency details. Experimental results on the SQUID real-world underwater open dataset demonstrate that, compared with eleven advanced algorithms, the proposed method exhibits high equilibrium and superiority in key metrics such as AG, IE, ENL, and UCIQE. Furthermore, its visual stability and robustness in complex and variable water environments are validated through the rank-sum composite evaluation method (RSCEM) and a refined scoring strategy.