基于自适应维纳滤波和2D-VMD的声呐图像去噪算法OACSTPCD

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.