Feature-preserving underwater image enhancement for visual measurement via dynamic graph and low-rank attention

Abstract Underwater imaging is often degraded by wavelength-dependent absorption and light scattering, leading to color distortion, multiscale blur, and spatially non-uniform attenuation. Such degradations not only reduce visual quality but also undermine the stability of feature extraction that many underwater visual measurement pipelines rely on. Existing methods often face challenges in maintaining robustness and preserving measurement-relevant structural cues under such complex degradation. We propose a dynamic graph and multi-head latent attention collaborative network (DGMLA-Net). Specifically, the dynamic graph feature enhancement module captures spatial dependencies to suppress noise and correct non-uniform degradations, while the low-rank efficient attention module enables global context aggregation and cross-scale feature fusion with reduced memory overhead. Experiments on multiple underwater benchmarks demonstrate that DGMLA-Net achieves competitive performance on standard image-quality metrics. More importantly, measurement-oriented evaluations show that the enhanced images improve the reliability of downstream feature processing, yielding more stable keypoint detection and more continuous edge extraction. These results indicate that DGMLA-Net provides feature-preserving underwater imagery as measurement-oriented preprocessing for subsequent vision-based measurement tasks.