Sonar image denoising method based on residual and attention network

Zhao Dongdong; Ye Yifei; Chen Peng; Liang Ronghua; Cai Tiancheng; Guo Xinxin

doi:10.12086/oee.2023.230017

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Zhao D D, Ye Y F, Chen P, et al. Sonar image denoising method based on residual and attention network[J]. Opto-Electron Eng, 2023, 50(6): 230017. doi: 10.12086/oee.2023.230017

Citation:

Zhao D D, Ye Y F, Chen P, et al. Sonar image denoising method based on residual and attention network[J]. Opto-Electron Eng, 2023, 50(6): 230017. doi: 10.12086/oee.2023.230017

Sonar image denoising method based on residual and attention network

1.
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 330063, China
2.
Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan 572000, China

Fund Project: National Natural Science Foundation of China Youth Science Foundation Project (62001418), Zhejiang Provincial Natural Science Foundation Project (LQ21F010011), Strategic Pioneering Science and Technology Special Project of Chinese Academy of Sciences (Class A) (XDA22030302), and Basic scientific research business expense special fund projects of Zhejiang universities (RF-C2019001)

More Information

^*Corresponding author: chenpeng@zjut.edu.cn

Received Date 20 January 2023

Revised Date 05 April 2023

Accepted Date 11 April 2023

Published Date 25 June 2023

Abstract

Abstract

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.
- forward looking sonar /
- image denoising /
- noise simulate /
- channel attention /
- dense residual

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References

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Overview

Overview

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 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 dual 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. The main contributions of this paper are as follows.

a) The dual channel attention module is used to estimate the noise, which adaptively accepts information of different scales through two paths using 3×3 and 5×5 convolutional kernels, respectively, and shunts these information to the next layer of neurons to enhance the feature extraction capability of the module, and then uses one-dimensional convolution to generate a channel attention map to extract the interdependencies between features maps, acquiring more information while reducing the computational volume.

b) Dense in residual module is used to remove noise. This module replaces the 3×3 convolutional layers in the traditional residual block with component group convolutions to reduce the number of parameters and improve the training speed, followed by dense and residual connections designed inside the convolutional kernel to learn the differences and connections between feature maps of different sizes, to transfer the learned information from each layer more smoothly and avoid performance degradation and the problem of gradient explosion and disappearance, and finally improve the multi-scale information extraction capability of the network at a fine granularity level.

In this paper, we simulate the generation of forward-looking sonar images by FieldII and add simulated multiplicative Rayleigh noise and additive Gaussian noise to generate a training set for training the network. Later, comparison experiments are conducted on the simulated data test set and the real data set, and the good performance of the proposed method is demonstrated in terms of PSNR, SSIM, and Brisquet image comparison metrics.