## 1 Introduction  
Autonomous Underwater Vehicles (AUVs) possess distinctive features such as unmanned and cable-free operation, as well as a wide range of mobility, making them extensively utilized in ocean observation, mine search, rescue missions, and marine military applications [1, 2]. During underwater exploration and operations, AUVs rely primarily on visual images as a means to gather scene information at short distances [3]. However, the quality of underwater visual images is considerably lower than that of land-based images, exhibiting issues such as blurring, color distortion, low contrast, and high noise [4]. Consequently, it has become imperative to enhance underwater images to enable effective target identification and localization by AUVs. Traditional methods for improving the clarity of degraded underwater images can be broadly categorized into two approaches: image restoration methods based on physical models and image enhancement methods based on non-physical models [5]. In the realm of underwater image restoration, researchers have drawn inspiration from the DCP algorithm [6]. This algorithm aims to clarify underwater images by estimating the degradation model and parameters, thereby inverting the imaging process [7; 8]. However, the diverse range of degradation types encountered in underwater images poses a challenge in determining a single imaging model with fixed parameters based on a priori assumptions. On the other hand, underwater image enhancement methods bypass the need to consider the imaging process directly. For instance, histogram equalization [9], a fundamental linear mapping method, enhances the image contrast by adjusting the total distribution through a constructed function. Methods rooted in Retinex theory [10] are employed to correct color deviations in underwater images, thus enhancing the overall color quality. Nevertheless, these techniques are prone to issues such as local over-enhancement and color inhomogeneity, leading to the introduction of new noise and color biases in the image. To address these limitations, image fusion algorithms [11] have been developed. These algorithms combine multiple enhanced underwater images with specific weights, effectively reducing noise and local oversaturation resulting from a single enhancement method. However, the requirement of preprocessing multiple enhanced images prior to fusion restricts the applicability of such methods in high real-time processing scenarios involving AUVs. In recent years, the rapid advancement of AI technology has propelled the rapid development of deep learning, which boasts powerful feature learning capabilities. Image processing methods based on deep learning have exhibited remarkable performance [12; 13; 14]. Consequently, numerous enhanced techniques have emerged within the domain of underwater image enhancement research [15; 16; 17; 18; 19; 20]. [PERSON] et al. [17] conducted experiments that demonstrate the commendable performance of the fusion base [11] on a diverse range of underwater images. Furthermore, they propose a gated fusion network called Water-Net, which enhances underwater images by leveraging white balance, histogram equalization, and gamma correction algorithms as inputs to the network model. Subsequently, the network model predicts corresponding confidence maps, which are then fused with multiple inputs to obtain the enhanced image. While this method effectively enhances the performance of image fusion, its network structure is intricate and it requires different algorithms to generate three types of augmented images as input data for the network model. Consequently, its practicality is significantly impacted, rendering it unsuitable for real-time augmentation of underwater images by AUVs. Several researchers have proposed the use of multi-scale feature fusion techniques to enhance network models for underwater image enhancement [21; 22; 23]. Unlike image fusion methods, these approaches do not require the fusion of multiple inputs for enhancement.