Underwater images are afflicted by dynamic blur, low illumination, poor contrast, and noise interference, hampering the accuracy of underwater robot proximity detection and its application in marine development. This study introduces a solution utilizing the MIMO-UNet network. The network integrates the Atrous Spatial Pyramid Pooling module between the encoder and the decoder to augment feature extraction and contextual information retrieval. Furthermore, the addition of a channel attention module in the decoder enhances detailed feature extraction. A novel technique combines multi-scale content loss, frequency loss, and mean squared error loss to optimize network weight updates, enhance high-frequency loss information, and ensure network convergence. The effectiveness of the method is assessed using the UIEB dataset. Ablation experiments confirm the efficacy and reasoning behind each module design while performance comparisons demonstrate the algorithm’s superiority over other underwater enhancement methods.