DeepSeaNet: An Efficient UIE Deep Network

Underwater image enhancement and object recognition are crucial in multiple fields, like marine biology, archeology, and environmental monitoring, but face severe challenges due to low light, color distortion, and reduced contrast in underwater environments. DeepSeaNet re-evaluates the model guidance strategy from multiple dimensions, enhances color recovery using the MCOLE score, and addresses the problem of inconsistent attenuation across different regions of underwater images by integrating a feature extraction method guided by a global attention mechanism by ViT. Comprehensive tests on diverse underwater datasets show that DeepSeaNet achieves a maximum PSNR of 28.96 dB and an average SSIM of 0.901, representing a 20–40% improvement over baseline methods. These results highlight DeepSeaNet’s superior performance in enhancing image clarity, color richness, and contrast, making it a remarkably effective instrument for underwater image processing and analysis.