Residual state-space networks with cross-scale fusion for efficient underwater vision reconstruction - Xiong, Nei; Zhang, Yuhan (Frontiers in Remote Sensing, 2025)
Underwater vision is inherently difficult due to wavelength-dependent light absorption, non-uniform 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.