Learning to Reorder: Value-Driven State Space Models for Underwater Image Enhancement.

State Space Models (SSMs) have emerged as a compelling backbone for vision tasks, offering linear complexity and global receptive fields. However, their application to Underwater Image Enhancement (UIE) is fundamentally constrained by the inherent mismatch between the standard sequential scanning mechanism and the unique statistical distribution of underwater scenes. Specifically, the dominance of large-scale, homogeneous background regions tends to overshadow the feature responses of sparse yet critical targets during state propagation, leading to the attenuation of semantic information and compromised structural reconstruction. To transcend this limitation, we propose VDSNet, a novel Value-Driven State Space Model framework. The core innovation lies in the Multi-Granularity Value Guidance Learning (MVGL) module, which generates a pixel-aligned value map to dynamically reorganize the scanning sequence, thereby prioritizing informative regions to ensure robust long-range state propagation for salient features. Complementing this, we design a Mamba-Conv Mixer (MCM) block that synergistically integrates priority-driven global sequencing with dynamic local convolutions, effectively balancing background suppression with target enhancement. Additionally, a Cross-Feature Bridge (CFB) is introduced to refine multi-level feature fusion. Extensive experiments demonstrate that VDSNet establishes a new state-of-the-art, surpassing WMamba by 0.89 dB on average while exhibiting superior water bias suppression and color fidelity. Furthermore, a lightweight variant, VDSNet-S, is constructed via efficient operators and resolution rescaling, offering a practical solution for real-time applications.