RD-UIE: Relation-Driven State Space Modeling for Underwater Image Enhancement
Journal:
arXiv
Published Date:
May 2, 2025
Abstract
Underwater image enhancement (UIE) is a critical preprocessing step for
marine vision applications, where wavelength-dependent attenuation causes
severe content degradation and color distortion. While recent state space
models like Mamba show potential for long-range dependency modeling, their
unfolding operations and fixed scan paths on 1D sequences fail to adapt to
local object semantics and global relation modeling, limiting their efficacy in
complex underwater environments. To address this, we enhance conventional Mamba
with the sorting-based scanning mechanism that dynamically reorders scanning
sequences based on statistical distribution of spatial correlation of all
pixels. In this way, it encourages the network to prioritize the most
informative components--structural and semantic features. Upon building this
mechanism, we devise a Visually Self-adaptive State Block (VSSB) that
harmonizes dynamic sorting of Mamba with input-dependent dynamic convolution,
enabling coherent integration of global context and local relational cues. This
exquisite design helps eliminate global focus bias, especially for widely
distributed contents, which greatly weakens the statistical frequency. For
robust feature extraction and refinement, we design a cross-feature bridge
(CFB) to adaptively fuse multi-scale representations. These efforts compose the
novel relation-driven Mamba framework for effective UIE (RD-UIE). Extensive
experiments on underwater enhancement benchmarks demonstrate RD-UIE outperforms
the state-of-the-art approach WMamba in both quantitative metrics and visual
fidelity, averagely achieving 0.55 dB performance gain on the three benchmarks.
Our code is available at https://github.com/kkoucy/RD-UIE/tree/main
