首页|基于自适应维纳滤波和2D-VMD的声呐图像去噪算法

基于自适应维纳滤波和2D-VMD的声呐图像去噪算法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
声呐图像易产生对比度低、分辨率低、边缘失真等问题,所以在去除声呐图像噪声时难以将有效信号与噪声准确分离,从而导致去噪后图像对比度降低、边缘轮廓不清晰、细节丢失严重等问题.本文提出一种基于自适应维纳滤波和2D-VMD(二维变分模态分解)的声呐图像去噪算法.首先通过二维变分模态分解对含噪图像进行分解,得到一系列不同中心频率的模态分量,利用相关系数和结构相似度筛选出有效的模态分量,并使用自适应维纳滤波处理有效的模态分量,最后将滤波后的模态分量进行重构,从而去除图像中的噪声.实验结果表明:所提图像去噪算法在相关系数(CC)、结构相似度(SSIM)这两项客观数据上表现最优,峰值信噪比(PSNR)略低于NSST域去噪,综合客观数据与视觉效果,本文所提算法去除噪声后的图像细节和边缘保持能力效果最佳.
Sonar image denoising algorithm based on adaptive Wiener filtering and 2D-VMD
Sonar images are prone to problems such as low contrast,low resolution,and edge distortion,so it is dif-ficult to accurately separate effective signals from noise when removing noise from sonar images,resulting in reduced image contrast,unclear edge contours,and severe detail loss after denoising.Therefore,this paper proposes a sonar image denoising algorithm based on adaptive Wiener filtering and 2D-VMD(Two Dimensional Variational Mode De-composition).First,a noisy image is decomposed using 2D-VMD to obtain a series of sub modes with different center frequencies.Effective modal components are obtained via correlation coefficients and structural similarity,then pro-cessed by adaptive Wiener filtering,and finally the filtered modal components are reconstructed to remove noise.The experimental results show that the proposed image denoising algorithm achieves the best results in terms of correla-tion coefficient and structural similarity,with a peak signal-to-noise ratio slightly lower than that of NSST domain de-noising.Taking into account objective data and visual effects,the algorithm proposed in this paper achieves the best performance in image details and edge preservation after removing noise.

image denoisingtwo dimensional variational mode decomposition(2D-VMD)adaptive Wiener filte-ringmodal componentsonar image

冯伟、刘光宇、刘彪、周豹、赵恩铭

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大理大学 工程学院,大理,671003

上海交通大学 海洋智能装备与系统教育部重点实验室,上海,201100

图像去噪 二维变分模态分解 自适应维纳滤波 模态分量 声呐图像

国家自然科学基金海洋智能装备与系统教育部重点实验室开放基金云南省地方本科高校基础研究联合专项资金云南省中青年学术和技术带头人后备人才项目

62065001MIES-2023-02202101BA070001-054202205AC160001

2024

10.13878/j.cnki.jnuist.20230407001

南京信息工程大学学报
南京信息工程大学

南京信息工程大学学报

CSTPCD北大核心
影响因子：0.737
ISSN：1674-7070
年,卷(期)：2024.16(1)
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