Multi-feature Collaborative Underwater Image Enhancement Based on Semantic Perception

A semantic-aware multifeature collaborative underwater image enhancement network is proposed for existing underwater image enhancement methods with poor defogging and color recovery effects. First, the texture and structure features of the original underwater image are fully extracted using the semantic-aware feature extraction module. Meanwhile, the degraded underwater image is input to the multiscale enhancement module for contrast enhancement and multiscale feature refinement, and global features of the enhanced image are extracted. Second, the multifeature fusion module modulates and fuses multibranch features in parallel to enhance the effective connection and feature. Finally, a clear underwater image is reconstructed by connecting residuals. Experimental results on real-world underwater datasets UIEB, EUVP, and UFO show that the proposed method has a better enhancement effect than classical algorithms based on pixel processing, physical models, and other deep learning algorithms. In terms of objective evaluation indicators, the peak signal-to-noise ratio and structural similarity of full-reference evaluation indexes are improved by 1.72% and 4.3%, respectively, and the information entropy of the no-reference evaluation index is improved by 0.32%.