基于残差和注意力网络的声呐图像去噪方法

前视声呐作为一种水下主动声呐设备常用于采集水下图像数据，然而会受到水下噪声的影响导致图像质量下降。针对这一问题，本文提出了一种基于密集残差和双通道注意力机制网络的前视声呐图像去噪方法。首先采用双通道注意力机制对声呐图像的通道信息进行提取，统计声呐图像的全局信息，输出声呐图像的噪声图；密集残差块根据噪声图和声呐图像，充分学习不同尺度上的特征信息，经过多次学习和信息传递后输出干净声呐图像。针对前视声呐图像及其噪声特点，模拟了前视声呐图像并添加瑞利分布的乘性噪声和高斯分布的加性噪声，生成模拟数据集用于网络训练和性能评估。在模拟数据集和真实数据集的实验中表明，本文方法能够有效去除噪声，保留图像细节。

Sonar image denoising method based on residual and attention network

As a kind of underwater active sonar equipment, forward-looking sonar is often used to collect underwater image data. However, it will be affected by underwater noise, which leads to the degradation of image quality. Due to this situation, a forward-looking sonar image denoising method is proposed based on dense residuals and a dual-channel attention mechanism network. Firstly, the two-channel attention mechanism is adopted to extract the channel information of the sonar image, collect the global information of the sonar image, and output the noise map of the sonar image. Based on the noise image and sonar image, the dense residual block fully learns the feature information at different scales and outputs a clean sonar image after multiple learning and information transfer. Because of the forward-looking sonar image and its noise characteristics, the forward-looking sonar image is simulated and the multiplicative noise of Rayleigh distribution and the additive noise of Gaussian distribution are added to generate a simulation dataset for network training and performance evaluation. Experimental results on the simulated data set and real data set show that the proposed method can effectively remove the noise and retain image details.