## 2 Materials and Methods
### Multi-Teacher Teaching  
To meet the requirements of real-time enhancement of underwater images by AUVs, achieving a balance between a high-quality output and a low complexity is crucial. Previous image fusion methods have relied on the merging of multiple enhanced images to synthesize clear underwater images, exploiting the benefits of information integration. However, such approaches often involve multiple pre-processing steps and image fusion network modules, resulting in increased computational demands. In order to address this challenge, we propose a novel approach that shifts the paradigm from \"multi-image fusion\" to \"multi-teacher teaching\". In this approach, the student model learns from multiple teacher models, enabling it to incorporate the complementary advantages of multiple enhanced underwater images. Consequently, our method eliminates the need for image preprocessing and image fusion network modules. Furthermore, the adoption of model compression in the lightweight student model enables a harmonious balance between performance enhancements and real-time capabilities. The concept of multi-teacher knowledge distillation resembles the cognitive process of human learning, where students deepen their understanding by receiving guidance from multiple teachers. The utilization of multiple teachers effectively enhances the performance of the student model. However, when there is a significant capacity gap between the teacher and student models, the transfer of knowledge from the teacher to the student model may  
Figure 2: The overall structure of MTUW-GAN. The different structural teacher generators provide complementary knowledge, and the Lightweight Student Generator (\(G_{S}\)) in the blue solid box learns to enhance underwater images under the guidance of multiple teachers (\(G_{T}^{M}\) and \(G_{T}^{A}\)). n in \(G_{T}^{M}\) denotes each channel of the convolutional layer divided by the channel compression factor. be insufficient, resulting in a degradation of the student model's performance [27]. To address this issue, our framework adopts a consistent MobileNet-Style [28] architecture for both the teacher and student models. This architectural consistency reduces the number of model parameters while ensuring effective knowledge transfer. [PERSON] et al. [29] demonstrated that the ResBlock layer in the ResNet-based generator [30] accounts for most of the model parameters and computational cost, remaining unaffected by decomposition. Consequently, to reduce model redundancy, we employ channel pruning techniques [31; 32] to decrease the channel width of the ResBlock layer. By applying channel pruning to the robust \(G_{T}^{M}\), we obtain a more streamlined \(G_{S}\). The specific settings are to set the channel compression factor n to 4 and to reduce the number of convolution filters of \(G_{T}^{M}\) from 64 to 16 for \(G_{S}\). This model compression approach not only improves computational efficiency but also provides a better foundation for subsequent model optimization and deployment. To achieve image fusion-level performance for \(G_{S}\), we incorporated the Assistance Teacher Generator (\(G_{T}^{A}\)) into the framework. Inspired by the residual network [33], we incorporate expansion residual blocks to facilitate additional nonlinear transformations that extract more expressive information features from underwater images. This aids in accurate recovery of details, colors, and contrasts in the underwater images. The robust \(G_{T}^{A}\) exhibits a faster convergence rate compared to the shallow \(G_{S}\), aligning with the cognitive process of teachers guiding students in the learning process. By engaging in collaborative training between the \(G_{T}^{M}\) and the \(G_{T}^{A}\), teacher generators with diverse structures can effectively capture the intricate relationships between input image features and augmented images from different perspectives. Consequently, the knowledge gained from these diverse perspectives can be transferred to the \(G_{S}\). This approach offers several advantages.