Degradation-Decoupling Vision Enhancement for Intelligent Underwater Robot Vision Perception System

Abstract

Underwater robots rely on high-quality visual data for precise monitoring and manipulation, yet complex underwater environments often degrade image quality through color distortion, texture blurring, and detail loss. Existing enhancement methods partially address these issues, but fail to effectively decouple nonlinear relationships among degradation factors, leading to inconsistent performance. To address these challenges, we propose a degradation-content decoupling-based underwater image enhancement network (DCDN). The framework integrates a super-fusion cascade module for dynamic feature weighting, reducing artifacts, and combines multichannel color space transformation with texture-guided correction to decouple and optimize degradation factors. This approach improves color fidelity and texture detail restoration by refining color information and adapting local textures. Experiments on public datasets demonstrate that DCDN outperforms existing methods in various underwater scenarios. This work enhances the visual capabilities of underwater robots, supporting intelligent transportation applications, such as marine logistics and underwater inspections.