一种改进的二维经验模态分解SAR图像噪声抑制方法

An Improved BEMD Method for SAR Image Noise Suppression

针对二维经验模态分解(bidimensional empirical mode decomposition,BEMD)算法在抑制SAR图像噪声的过程中造成的边界污染问题,提出了一种改进的BEMD图像去噪方法。该方法在对含噪SAR图像进行BEMD分解的过程中,首先,对极值点进行三角剖分,同时在插值时进行边界周期延拓;其次,对插值后的曲面再进行IMF分解;然后,对分解后的含噪IMF分量进行小波滤波处理;最后,重构图像,从而达到抑制边界污染和去除斑点噪声的目的。实验结果表明,此方法同传统BEMD方法相比,边界污染抑制效果明显,在有效抑制SAR图像噪声的同时,较好地保持了图像的边缘和细节信息。

For boundary pollution problems occurring during the process of SAR image noise suppression by bidimensional empirical mode decomposition （BEMD）, an improved BEMD method of image denoising is proposed. In this method, the noisy SAR image is decomposed into detail parts and contour parts using BEMD. First, the extreme points are triangulated, and interpolation boundaries equivalents are extended. Then, the surfaces of the IMF components after interpolation are decomposed, and the IMF components with noise after the decomposition are processed by wavelet filtering. Finally, images are reconstructed. Experimental results show that this method effecive for suppressing speckle noise, as compared to the bidimensional empirical mode decomposition, and has sufficiently advantages when retaining edges and detail information while suppressing noise.