# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 1 Introduction  
In recent years, the exploration of the oceans has been significantly facilitated by the advancements in underwater robotics [1]. Underwater robots are commonly equipped with visual sensing devices that capture information about the surrounding environment and record it using images as data carriers [2]. Various underwater tasks, including submarine pipeline cleaning and mineral exploration [3, 4], rely on the analysis of underwater images. However, the complexity of underwater environments introduces challenges such as color deviation and low contrast in underwater images due to wavelength- and distance-related attenuation and scattering. When light travels through water, it undergoes selective attenuation, leading to varying degrees of color deviation. Additionally, suspended particulate matter, such as phytoplankton and non-algal particles, scatters light, further reducing contrast. Based on recent research findings, we categorize the existing underwater image enhancement methods into three main categories: non-physical model-based, physical model-based, and deep learning-based approaches [5]. Early non-physical model-based enhancement methods primarily focused on adjusting the pixel values of underwater images in order to enhance image presentation. Thesemethods included techniques such as histogram equalization, white balance adjustment, and image fusion. While these non-physical model-based methods have the potential to improve visual quality to some extent, they overlook the underlying underwater imaging mechanism [6]. As a result, these methods often produce over-enhanced results or introduce artificially created colors. For instance, [PERSON] et al. [7] propose a UCM algorithm that initially performs color equalization on underwater images in the RGB color space and then corrects the contrast in the HSV color space. However, these algorithms are characterized by their simplicity, which makes them susceptible to noise over-enhancement, as well as the introduction of artifacts and color distortion. The predominant methods for enhancing underwater images are based on physical models [6,8]. These models involve specific mathematical representations of the imaging process, enabling the estimation of unknown parameters, and subsequently producing clear images by removing the influence of the water body. Among these methods, the Dark Channel Prior (DCP) algorithm [9] is considered a classical approach. It establishes a relationship between land-based foggy images and the imaging model, enabling the estimation of light wave transmittance and atmospheric light, thus facilitating the restoration of foggy images. Given the similarity between the underwater imaging process and the fogging process, the DCP algorithm can also be applied to correct distorted underwater images. However, it is important to note that the application of this algorithm is limited, and the enhancement results are prone to introducing new distortion problems. Furthermore, [PERSON] et al. [10] introduced the Underwater Dark Channel Priority (UDCP) algorithm, designed specifically for underwater scenarios. This algorithm considers the attenuation characteristics of light wave transmission in water, allowing for the estimation of a more accurate transmittance distribution. However, due to the complexity and variability of the underwater environment, constructing a precise and universally applicable imaging model becomes challenging. Moreover, parameter estimation is susceptible to bias, resulting in less satisfactory enhancement results. [PERSON] et al. [11] employed a modeling approach that considers image blurriness and light absorption (IBLA) to recover underwater images. [PERSON] et al. [12] utilized the underwater light attenuation prior (ULAP) information to estimate the scene's depth map, followed by the enhancement of the underwater images. However, it should be noted that these methods heavily rely on specific prior conditions and may not be effective for type-sensitive underwater image enhancement.