Enhancing Underwater Imagery with Feature-Parallel Multi-Scale Attention and Spatially Enhanced Global Representation Transformer

In recent years, advancements in underwater imaging technologies have played a pivotal role in expanding our understanding of the Earth's aquatic environments. Nevertheless, underwater imagery frequently presents a plethora of challenging conditions, including but not limited to limited visibility, fluctuations in ambient light, and color distortions. These factors pose substantial obstacles to the effective analysis and interpretation of images in aquatic environments. Addressing these challenges necessitates innovative approaches that can enhance the quality of underwater imagery and unlock valuable insights hidden beneath the surface. This paper introduces a methodology designed to revolutionize the field of Underwater Image Enhancement (UIE) - the Feature-Parallel Multi-Scale Attention Transformer (FPMAT) and Spatially Enhanced Global Representation Transformer (SEGRT). By combining cutting-edge techniques from computer vision, attention mechanisms, and global representation learning, our proposed framework aims to significantly improve the clarity, detail, and interpretability of underwater images. The FPMAT component is designed to selectively focus on relevant image features at multiple scales, allowing the model to attend to both fine details and global contextual information adaptively. SEGRT is designed to capture global information and strengthen its focus on significantly deteriorated regions.