MSG-Net: Structure-Guided Enhancement for Underwater Images based on Multi-view Feature Interaction

Underwater images often suffer from blurring, low contrast, and color distortion. Although current multi-view methods show better potential than single-input methods in mitigating these degradations, they still struggle to fuse and extract features across views. Moreover, most existing methods struggle to capture fine texture details and preserve key structural information, resulting in images with insufficient detail and unclear structures. To address these issues, we propose a Multi-View Input and Structure-Guided Network, MSG-Net, to achieve underwater image enhancement. Specifically, White Balance (WB) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are employed to construct multi-view input, enabling more effective handling of diverse underwater degradation factors. A Cross-Channel Attention Fusion (CCAF) module is designed to effectively fuse these multi-view features via a channel-wise attention mechanism. Additionally, a Channel-Group Axial Transformer (CGAT) is introduced to extract axial features within channel groups, enhancing cross-channel interactions while alleviating the patch boundary discontinuities in Transformer-based methods. Furthermore, a Structure-Guided Transformer (SGT) is proposed to incorporate structural information into the enhancement process, enriching texture details and highlighting key structures. Extensive experiments demonstrate that MSG-Net outperforms or matches state-of-the-art underwater image enhancement methods across various benchmarks. Moreover, MSG-Net exhibits outstanding performance on other enhancement tasks.