## 3 Proposed Method  
### Conditional Information Fusion Module  
For some image generation tasks, especially sonar image simulation in complex environments, different types of sonar targets and background information should be considered to generate a condition-specific sonar image. A conditional GAN [20] method based on multi-layer perceptron was proposed, but the image generated by the multi-layer perceptron structure had some problems [15], such as noise interference, low fidelity, and a strange structure. It could not cope with the task of high-fidelity sonar image generation in a complex environment. In this paper, a conditional information fusion module is designed to fuse the conditional information of the encoded sonar image with the original input of the network, and then the fused information is further input into the network. The overall framework of the method is shown in Figure 1. The conditional information fusion module fuses the noise with the encoded conditional information of different categories, M represents the number of category conditions, and then transmits the fused information to the generator. The generator main network is composed of multiple deconvolution modules and a dual-branch attention feature fusion module. The feature map generated by the deconvolution module is processed by the dual-branch attention feature fusion module and then transferred to the next deconvolution module. The dual-branch attention feature fusion module completes the feature-level fusion operation through two different attention calculations combined with the layer-skipping operation. The conditional information fusion module fuses image information with category information and transmits it to the discriminator network. The discriminator main network is composed of multiple convolution modules and a dual-branch attention feature fusion module, that performs the difference measurement between the true and false distributions.  
Figure 1: The overall network framework of the method in this paper.  
The conditional information fusion module is located on the input side of the generator and the discriminator. The fusion process is as follows:  
In the discriminator network structure, given the input image \(\text{A}\in\mathbb{R}^{H\times W\times C}\) and label \(\text{L}\in\mathbb{Z}\), \(\text{L}\) is converted to \(\text{L}\in\mathbb{R}^{N}\) through the embedding operation, where \(N=H\times W\times C\) represents the number of all pixels in the image, and the input sonar image \(\text{A}\) is tiled into \(\text{A}\in\mathbb{R}^{N}\) through the flatten operation; \(\text{A}\) and \(\text{L}\) are multiplied element-wise to obtain the vector \(\text{B}\in\mathbb{R}^{N}\) after the label and image information are fused, and the final input \(\text{B}\)\(\in\mathbb{R}^{H\times W\times C}\) of the discriminator is obtained by further performing the reshape operation.  
In the generator network structure, given the input random noise \(\text{Z}\in\mathbb{R}^{M}\) and label \(\text{L}\)\(\in\mathbb{Z}\), \(\text{L}\) is converted to \(\text{L}\in\mathbb{R}^{M}\) through the embedding operation, where \(\text{M}\) is the size of the noise dimension, and then \(\text{Z}\) and \(\text{L}\) are multiplied element-wise to get the final input \(\text{D}\)\(\in\mathbb{R}^{M}\) of the generator.