Underwater image enhancement network with spatial and frequency domain dual path fusion

Underwater images play a crucial role in underwater exploration tasks. However, due to the unique physical and chemical properties of the underwater environment, underwater images often suffer from issues such as low contrast, color cast, and blurriness. To address these challenges, this paper proposes a dual-path fusion model (UW-DSFNet) for underwater image enhancement. The model aims to extract both color and texture features from underwater images comprehensively, utilizing spatial and frequency domains. In the spatial domain path, a low-complexity NAFNet network is employed along with gate residual and GELU activation functions to extract color features from the images. In the frequency domain path, an MLP framework is utilized, and Fourier transform is applied to obtain frequency domain texture feature maps. Finally, the features extracted from the spatial and frequency domains are fused, followed by a detail enhancement process. Experimental results demonstrate that the proposed model effectively enhances underwater images, producing clear and visually appealing results with rich colors.