## 1 Introduction
Instead, they leverage the feature information from the intermediate layer of the neural network and fuse multi-scale feature maps generated through gradual convolution of underwater images within the network. This fusion process aims to improve the overall performance of underwater image enhancement. [PERSON] et al. [21] introduced MLFcGAN, a multiscale feature fusion framework. This framework involves extracting feature maps at different scales by gradually downsampling the input image using a convolutional layer. The fused features are then fed into the corresponding decoder layer, which incorporates a feature fusion module and skip-connect operation. The resulting enhanced image is obtained through multilevel feature fusion. [PERSON] et al. [23] proposed an end-to-end framework called heterogeneous feature fusion and dynamic feature enhancement. This framework integrates a feature fusion module with an attention mechanism, enhancing the encoder structure by introducing an improved feature attention module. Features are gradually extracted at different scales, ranging from low to high levels, and the decoder employs an upsampling operation to reconstruct feature vectors and progressively restore them to clear images. Furthermore, [PERSON] et al. [24] presented MGF-cGAN, a multiscale gated fusion framework for underwater image enhancement. The generator in this framework utilizes a multiscale feature extraction module to extract feature information at different scales from three parallel sub-networks. A gated fusion module (GFM) is introduced to gradually fuse the three feature images collected from the multiscale feature extraction module using a recursive strategy, thereby improving contrast and color saturation in underwater images, while the multiscale feature fusion methods eliminate the need for preprocessing multiple enhanced images. The operations involved, such as multiscale feature extraction, fusion modules, and dense connectivity in the network, increase the complexity and the number of parameters. Consequently, these algorithms struggle to strike a balance between low computation requirements and high-quality image enhancement in hardware-constrained practical applications. Among the available underwater image enhancement methods, image fusion methods and multi-scale feature fusion methods exhibit superior performance. However, their network models employ complex multi-branch structures with a substantial number of parameters. This poses a challenge for AUVs equipped with small-capacity batteries, as they often have a limited computational power and storage capacity. Consequently, these advanced algorithms are impractical for local real-time applications. In underwater environments, effective real-time image processing algorithms are essential. A typical AUV camera captures and transmits between 10 and 30 frames per second. To ensure a seamless underwater observation experience for operators, the processing time for each image should ideally be less than 0.1 s. Therefore, there is a pressing need for a comprehensive conceptual and structural enhancement of fusion-based algorithms that can deliver both an improved performance and real-time capabilities. In the domain of image dehazing, [PERSON] et al. [12] proposed the Online Knowledge Distillation Network (OKDNet) as a solution. This approach involves the construction of a multiscale feature extraction network utilizing residual dense blocks guided by an attention mechanism. The network generates rich features, which are then sent to different branches for further processing. Supervised training is employed to generate two styles of dehazed images, which are subsequently fused using parallel convolution. Additionally, the model is optimized through online knowledge distillation, resulting in a reduction in the model parameters to 2.58 M. This optimization significantly enhances the algorithm's real-time applicability. Hence, employing knowledge distillation for model compression in real-time underwater image enhancement is a viable approach.