## 1 Introduction
[19] segmented underwater grayscale images by fusing the geodesic active contour model (GAC) and the Chan-Vese (CV) model. However, this method required the target region of the underwater image to have a uniform grayscale. [PERSON] et al. [20] integrated the transmission map and the saliency map into a unified level set formulation to extract the salient target contours of the underwater images. [PERSON] et al. [21] proposed some spatially varying regularization methods by using local image features (such as textures, edge, noise, etc.) to prevent excessive regularization in smooth regions and preserve spatial features in nonsmooth regions. However, it is difficult for this method to obtain accurate regularization parameters for underwater images with low contrast and blurred texture information due to blurred local features, such as textures and edges. [PERSON] et al. [22] presented an efficient approach for unsupervised segmentation based on image feature extraction to segment natural and textural images. Unfortunately, this method is unsuitable for natural underwater images with weak texture information. As a new technology of image processing, the neural network has also been used for underwater image segmentation. [PERSON] et al. [23] proposed the use of photorealistic synthetic imagery to train deep encoder-decoder networks. This method synthesized virtual underwater images, and each rendered image had a corresponding ground truth per-pixel label map. The mapping relationship between the underwater images and the segmented images was established by training the encoder-decoder network. [PERSON] et al. [24] proposed a deep neural network architecture for underwater scene segmentation. The architecture extracted features by pre-training VGG-16 and learned to expand the lower resolution feature maps using the decoder. The neural network achieved certain results in underwater image segmentation, but the lack of underwater data sets with corresponding functions is still a problem. Most of the existing underwater image segmentation methods are used to segment images with high foreground-background contrast and single background grayscale. When the underwater images with varying background grayscale and the targets have complex textures, the segmentation results of the above methods are not satisfactory. To address the above problem, we propose a novel dual-fusion model with semantic information for salient object segmentation of underwater images with complex backgrounds. In summary, the contributions of our model are as follows:  
1. We introduce saliency maps as semantic information to segment foreground information and background information;
2. The dual-fusion energy equation is proposed to extract the contours of saliency targets by integrating the local and global intensity fitting term;
3. For the missing saliency target information, we propose the correction module to correct the saliency target contour error by introducing the original image contour information. This paper is organized as follows: Section 1 reviews related works. In Section 2, we introduce in detail the derivation process of the dual-fusion model. Section 3 shows the experimental process, and we compare the proposed method with state-of-the-art segmentation methods, and the results demonstrate the superiority of the proposed methods. The conclusion of this paper is shown in Section 4.