## 1 Introduction
Furthermore, [PERSON] et al. [25] proposed that a student can learn better from multiple teachers, who are more informative and instructive than a single teacher. Motivated by their work, we present a novel framework called multi-teacher knowledge distillation GAN (MTUW-GAN) for underwater image enhancement. Through extensive experiments, we demonstrate that MTUW-GAN surpasses existing methods in terms of visual quality, model parameters, and complexity. Moreover, it effectively balances an enhanced performance with real-time capabilities. In conclusion, fusion-based methods for underwater image enhancement exhibit a superior performance and have been extensively studied. While these methods contribute to enhancing the overall image quality, there are several important considerations to address in underwater image enhancement scenarios:  
* The requirement to preprocess multiple enhanced images in the image fusion algorithm hampers practical usability and necessitates innovative approaches to the overall concept. * The inclusion of feature extraction branches and fusion modules in the multi-scale feature fusion method elevates computational demands and compromises the real-time performance of the algorithm. * The image translation loss function exhibits limited capability in rectifying color bias and facilitating detail recovery when employed for underwater image enhancement. * Previous fusion-based underwater image enhancement algorithms encounter challenges in striking a balance between enhancement effectiveness and real-time performance, with the need to process each image within a time frame of 0.1 s.  
To tackle these challenges, we aim to develop AUV underwater image enhancement algorithms that meet the demand for a high-quality output while ensuring real-time performance. In this regard, we introduce MTUW-GAN as our proposed solution. The key innovations of our approach are as follows:* We propose a framework called MTUW-GAN, which changes the idea of \"multi-image fusion\" to \"multi-teacher teaching\", and guides the student network to perform underwater image enhancement through multiple teacher networks. * We incorporate the attention mechanism to extract the channel attention information from the teacher network and transfer it to the middle layer of the student network in the form of a loss function, which improves the performance without increasing the number of parameters and computations of the student network. * We formulate a multimodal objective function to fully restore the visual quality of underwater images by removing noise and color distortion caused by underwater light scattering based on the global content, color, and texture information of the images. * We comprehensively verify that the method in this paper can achieve image fusion level performance by designing comparison experiments and ablation experiments, taking into account the performance enhancement and real-time performance. To provide a more intuitive illustration of the innovative ideas presented in this paper, particularly in comparison to previous image fusion and multi-scale feature fusion methods, Figure 1 showcases the overall process enhancement achieved by the proposed method. The remainder of this paper is structured as follows: In Section 2, we present an introduction to MTUW-GAN. Section 3 conducts quantitative and qualitative analyses, along with a comprehensive ablation experiment. Lastly, Section 4 provides a summary of our work.