## 1 Introduction  
In recent years, the development and utilization of the ocean have gradually become an important development direction. Since underwater vision research is the basis of marine-related disciplines, the rapid development of underwater image processing technology is inevitable [1, 2]. Image segmentation is a basic method of target extraction, which aims to partition an image into several constituent regions. Each region has coherent intensities, colors, and textures [3]. Furthermore, image segmentation can provide technical support for target tracking, image restoration [4, 5, 6], and other tasks. Now, some results have been achieved in underwater image segmentation. [PERSON] et al. [7] proposed an improved level set algorithm based on the gradient descent method and applied it to segment underwater biological images. [PERSON] et al. [8] improved the K-means algorithm to segment underwater image backgrounds and addressed the issue of improper K value determination. Moreover, this algorithm can minimize the impact of the initial centroid position of a grayscale image. [PERSON] et al. [9] used a canny edge detection algorithm to segment underwater images, whereas background noise greatly affected the canny edge detection algorithm. [PERSON] et al. [10] and [PERSON] et al. [11] used fuzzy C-means to segment underwater images. [PERSON] et al. [12] used the K-means algorithm to segment underwater images. However, the clustering algorithms mentioned above are greatly affected by the local gray unevenness of underwater images. Moreover, clustering algorithms contain local convergence errors and are only suitable for underwater images with a single background gray level. Some investigators have segmented underwater images based on their optical properties and achieved good results. For example, [PERSON] et al. [13] proposed an optical feature extraction, calculation, and decision method to identify the collimated region of artificial light and employed a level set method to segment the objects within the collimated region. This method could better identify the target region, but the level set method could not filter out background noise when the target region contained background information. [PERSON] [14] et al. proposed an RGB(red, green, blue) color channel fusion segmentation method for underwater images. The proposed method obtained the grayscale image with high foreground-background contrast and employed the thresholding segmentation method to conduct fast segmentation. However, the disadvantage of this method is that when the color of the background region is similar to the foreground region, the target cannot be segmented. Active contour models have also been used for underwater image segmentation. [PERSON] et al. [15] used the cluster-based algorithm for co-saliency detection and highlighted salient regions in the underwater images. Then, the local statistical active contour model was used to extract the target contours of underwater images. [PERSON] et al. [16] proposed an improved method based on the active contour model. The method used the RGB color space and contrast limited adaptive histogram equalization (CLAHE) to increase the contrast of the sea cucumber thorns and body. Then, the method extracted the edge of the sea cucumber thorns using the active contour model. [PERSON] et al. [17] improved the traditional level set methods by avoiding the calculation of the signed distance function (SDF) to segment underwater images. The improved method could speed up the computational complexity without re-initialization. [PERSON] et al. [18] proposed a method based on morphological component analysis (MCA) and adaptive level set evolution to segment underwater images. The MCA decomposes the image into texture and cartoon parts sparsely. The new adaptive level set evolution method combines the threshold piecewise function with the variable right coefficient and halting speed function and was used to obtain the edges of the cartoon part. [PERSON] et al.