# Multi-Module Combination for Underwater Image Enhancement  
[PERSON]  
1 Shanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China; [EMAIL_ADDRESS] (Z.J. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (G.H.) 1 Shanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China; [EMAIL_ADDRESS] (Z.J. ); [EMAIL_ADDRESS] (H.W. ); skzhg@163. Underwater environments possess unique characteristics, such as low light levels, turbid water, and object reflections, which present significant challenges for the acquisition and processing of underwater images [3]. The imaging quality of underwater images is susceptible to complex hydrodynamic effects, which are mainly characterized by typical degradation features such as non-smooth noise interference, non-uniform optical blurring and multi-spectral attenuation [4]. Degraded features in underwater images can significantly reduce the localization accuracy of target detection, weaken the robustness of feature recognition, and lead to the accumulation of trajectory drift errors during dynamic tracking. By enhancing underwater images, the quality can be significantly improved, thereby increasing the reliability and utility of underwater vision systems. Underwater image enhancement, a crucial technology at the intersection of computer vision and marine optics, aims to reconstruct high-quality images with physical interpretability. This is achieved by establishing a mapping relationship between degradation models and visual perception, thereby overcoming the limitations imposed by complex underwater environments on the effectiveness of visual systems. In response to the technical challenges presented by complex underwater imaging environments, researchers have proposed a range of innovative solutions in recent years. One such approach is a filter-based single-image defogging method that utilizes a guided joint bilateral filtering technique for haze removal [5]. The proposed algorithm excels in enhancing underwater images, achieving substantial improvements in both perceptual quality and objective evaluation metrics through effective contrast enhancement and noise suppression. Recent advances in deep learning have dramatically transformed underwater image enhancement methods, facilitating overall quality improvement through an end-to-end learning framework. In particular, the developed enhancement network is trained on a reference dataset and demonstrates exceptional capabilities in image processing [6]. Based on the insights gathered from our predecessors, we propose an architecture that first assesses whether to correct the image for color and white balance, depending on the outcomes of color deviation detection. Subsequently, it performs visibility restoration and contrast enhancement processes, followed by a fusion step to create an enhanced underwater image. Figure 1 illustrates the results of the visualization of the enhancement effect of this method in real and simulated underwater scenes in comparison. The main innovation of this study is reflected in the following dimensions:  
1. In this paper, we present an algorithm that utilizes color bias detection to identify color bias in images. A white balance technique is then applied to process the color-biased images. This preprocessing step enhances the detection of color bias, thereby improving the accuracy of subsequent image processing tasks. 2. We employ a defogging and contrast enhancement algorithm that utilizes a rank-one prior matrix and curve transformation. The clarity and legibility of the underwater image are improved by converting it to the LAB color space, removing fog with the rank-one prior matrix, and enhancing the image's contrast through a curve transformation. 3.