融合多尺度边缘检测的小波贝叶斯SAR图像滤波

An Wavelet Bayesian SAR Image Filtering with Multiscale Edge Detection

针对实测的SAR图像被噪声广泛淹没、传统滤波方法易模糊边缘等问题,提出了一种新的滤波方法。该方法在图像多尺度的小波分量上,将基于贝叶斯理论对不同系数和不同方向上设置不同阈值得到消噪后的各分量与基于多尺度边缘检测提取的图像边缘等结构所对应小波分量加权融合,重构输出。以真实的SAR影像进行对比实验后,选取图像的均值、等效视数、边缘保持指数、信噪比及特征地物的像素灰度曲线作为评价指标,对不同的滤波方法进行了综合量化评价。实验结果表明,该方法抑制SAR图像斑点噪声的效果较好,对边缘有较好的保持效果。

A new method of speckle filtering is presented for the problems that SAR image is widely submerged by noise and the traditional methods can cause the loss of edge information.Based on the Bayesian theory,different components and different directions are set to different thresholds to get the denoised components on multi-scale wavelet components.At the same time,extract wavelet components corresponding to structures such as image edges by multi-scale edge detection.Eventually,fuse these reconstructions to filtering noise.Finally,the new method is compared with existing methods using SAR image,and the different filtering methods are evaluated in quantitative analysis by using the indices that evaluate image quality such as mean,equivalent number,edge retention of the image,edge retention index,etc.The experimental results show that the proposed method has better effect on filtering speckle noise in SAR images,and it can preserve edge information more effectively.