基于聚类分析和旋转的改进的SAR图像PPB去斑

Improved Probabilistic Patch-based SAR Image Despeckling Based on Cluster Analysis and Rotation

PPB滤波器不能在滤波过程中对参与滤波的像素块进行有效的选择并具有不适宜的权重计算方式,从而导致滤波后的图像抑制了原图中尺寸较小的图像细节。针对以上问题,首先引入簇树这一数据结构,选取与PPB滤波器相同的距离准则构建簇树,以实现对图像块的快速、精确的筛选。然后通过旋转像素块重新定义两个像素块之间的权重,解决原始的PPB滤波器对图像中旋转的或镜像的重复区域不能很好利用的问题。最后采用PPB滤波器的非迭代滤波方式进行滤波。实验证明,改进的滤波器在纹理和细节保持方面较原滤波器有显著的提高,特别是在尺寸较小的图像细节特征保持方面。

Thin details in the filtered images are suppressed by the probabilistic patch-based （PPB） filter, which is at- tributed to the absence of effective selection of pixel patches and the unsuitable method of weight computing. For these problems, the data structure of cluster tree was introduced firstly. The same distance measure as applied in the PPB fil- ter was chosen to build the cluster tree, which allows for efficient and precise selection of similar patche~ Since the origi- nal PPB filter could not handle rotated or mirrored repetitive regions properly, the weight between two patches was re- defined after the rotation of the patches. Finally, the PPB （non-it） filter was used for the denoising. Experimental results show that the improved filter has better performance in texture and details preservation than the original PPB （non-it） filter, especially in retaining thin details.