Residual State-Space Networks with Cross-Scale Fusion for Efficient Underwater Vision Reconstruction

Nei Xiong¹Yuhan ZHANG^2*

• ¹Capital Normal University, Beijing, China
• ²Zunyi Medical University - Zhuhai Campus, Zhuhai, China

Underwater vision is inherently difficult due to wavelength-dependent light absorption, nonuniform illumination, and scattering, which collectively reduce both perceptual quality and task utility. We propose a novel architecture (ResMambaNet) that addresses these challenges through explicit decoupling of chromatic and structural cues, residual state-space modeling, and cross-scale feature alignment. Specifically, a dual-branch design separately processes RGB and Lab representations, promoting complementary recovery of color and spatial structures. A residual state-space module is then employed to unify local convolutional priors with efficient long-range dependency modeling, avoiding the quadratic complexity of attention. Finally, a cross-attention–based fusion with adaptive normalization aligns multi-scale features for consistent restoration across diverse conditions. Experiments on standard benchmarks (EUVP and UIEB) show that the proposed approach establishes new state-of-the-art performance, improving colorfulness, contrast, and fidelity metrics by large margins, while maintaining only ∼0.5M parameters. These results demonstrate the effectiveness of residual state-space modeling as a principled framework for underwater image enhancement.