A novel edge-feature attention fusion framework for underwater image enhancement

Abstract

Underwater images captured by Remotely Operated Vehicles are critical for marine research, ocean engineering, and national defense, but challenges such as blurriness and color distortion necessitate advanced enhancement techniques. To address these issues, this paper presents the CUG-UIEF algorithm, an underwater image enhancement framework leveraging edge feature attention fusion. The method comprises three modules: 1) an Attention-Guided Edge Feature Fusion Module that extracts edge information via edge operators and enhances object detail through multi-scale feature integration with channel-cross attention to resolve edge blurring; 2) a Spatial Information Enhancement Module that employs spatial-cross attention to capture spatial interrelationships and improve semantic representation, mitigating low signal-to-noise ratio; and 3) Multi-Dimensional Perception Optimization integrating perceptual, structural, and anomaly optimizations to address detail blurring and low contrast. Experimental results demonstrate that CUG-UIEF achieves an average peak signal-to-noise ratio of 24.49 dB, an 8.41% improvement over six mainstream algorithms, and a structural similarity index of 0.92, a 1.09% increase. These findings highlight the model’s effectiveness in balancing edge preservation, spatial semantics, and perceptual quality, offering promising applications in marine science and related fields.