斑点噪声是影响侧扫声呐图像质量的主要因素,降斑处理对侧扫声呐图像的判别与分析非常重要。针对侧扫声呐图像自身特性和斑点噪声分布特点,提出一种基于3维块匹配(BM3D)的降斑方法。根据海底散射模型,得到侧扫声呐图像斑点噪声的瑞利分布模型,然后通过高斯光滑函数幂变换将瑞利分布的噪声转化为高斯分布,通过对数变换将乘性噪声转变为加性噪声,再进行自适应的BM3D滤波,最后采用逆变换得到降斑图像。实验结果表明,该方法在降噪、边缘和纹理保持等方面均优于空间域、小波域、Curvelet域的一些降斑方法。

Speckle noise is the major factor affecting the quality of side-scan sonar images. Side-scan sonar image despeckling has therefore a great significance to object identification and image processing.According to side-scan sonar image characteristic and distribution of speckle noise,a despeckling method based on block-matching and 3D filtering (BM3D) is proposed in this paper.A Rayleigh distributed multiplicative speckle noise model is established according to a seabed scattering model.Rayleigh distributed of speckle noise is changed into Gaussian distribution by the Gaussian smooth function power transform.The multiplicative noise is changed into additive noise by a logarithmic transform.Then,the transformed images are filtered by an efficient BM3D algorithm.Last,the final images are achieved by inverse transform.The experimental results show that the new algorithm has a better performance in terms of edge preserving and denoising than other spatial filtering,wavelet and Curvelet domain filtering methods.