Side-scan sonar technology has been widely used as a major submarine detection method. However, the image noise through strictly improved still can't be eliminated completely, which decreases the accuracy of image interpretation. Four methods are examined to reduce or eliminate the side-scan sonar image noise, including directional diffusion based on the partial differential equations, regularization PM (AOS algorithm) , directional diffusion based on discrete wavelet, directional diffusion based on lifting wavelet. Compared with traditional methods, such as mean filtering, median filtering, Wiener filtering, wavelet soft threshold, hard threshold and Bayesian method of estimating threshold, it is found that the directional diffusion based on lifting wavelet method can not only improve the PSNR, but also maintain smoothness index and edge-preserving index. It is more suitable for side-scan sonar image denoising.%侧扫声纳作为一种重要的海底探测技术,已得到广泛的使用,但声强数据经过严格的数据处理后,依然存在噪声,影响图像的正确判读.针对侧扫声纳图像中的噪声问题,采用基于偏微分方程的方向扩散、正则化P-M(AOS算法)、基于离散小波的方向扩散、基于提升小波的方向扩散4种方法,与传统的均值滤波、中值滤波、维纳滤波和小波软阈值、硬阈值、贝叶斯估计阈值的方法进行实验对比,发现基于提升小波的方向扩散方法,不仅能有效提高峰值信噪比,而且还能保持较好的平滑指数和边缘保持指数,更适用于侧扫声纳图像的去噪处理.
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