激光与光电子学进展, 2018, 55 (4): 041004, 网络出版: 2018-09-11   

基于聚类三维块匹配的合成孔径雷达影像滤波算法 下载: 1102次

Synthetic Aperture Radar Image Filtering Based on Clustering Three-Dimensional Block-Matching
作者单位
武汉理工大学资源与环境工程学院, 湖北 武汉 430070
摘要
三维块匹配(BM3D)算法能够有效抑制平稳信号中的噪声。对于具有随机特性的合成孔径雷达影像斑点噪声,受限于三维变换阈值单一和在局部邻域寻找相似块,BM3D算法的滤波效果不佳。提出了基于K-Means聚类的BM3D算法,并将其应用于合成孔径雷达影像斑点噪声抑制。对图像块集合根据均值、方差和极差值构建的特征向量进行聚类,估计每一类块的噪声方差,根据类噪声方差估计自适应三维变换阈值;在每一个图像块类内部寻找相似块,实现全局相似块的快速查找。实验结果表明,同BM3D算法和非局部均值算法相比,所提算法具有更好的视觉效果和更高的峰值信噪比。
Abstract
Three-dimensional block-matching (BM3D) algorithm can effectively suppress the noise in stationary signal. However, it is not feasible for the speckle noise in synthetic aperture radar (SAR) image with random characteristics due to the single 3D transform threshold and the local neighborhood for searching similar blocks. We propose a BM3D algorithm based on K-Mean clustering for SAR image denoising. First, we calculate the feature vector according to the mean, variance, and poor value, and estimate noise variance of each image block. The adaptive 3D transform threshold will be determined through the estimated noise variance. Second, we can find similar image blocks of reference image block in the corresponding class of image blocks, and can find global similar image blocks quickly. The experiments demonstrate that the proposed algorithm achieves better visual effect and and higher peak signal to noise ratio than the BM3D algorithm and non-local mean algorithm.

詹云军, 代腾达, 黄解军, 董玉森, 叶发旺, 唐聪, 王萌. 基于聚类三维块匹配的合成孔径雷达影像滤波算法[J]. 激光与光电子学进展, 2018, 55(4): 041004. Yunjun Zhan, Tengda Dai, Jiejun Huang, Yusen Dong, Fawang Ye, Cong Tang, Meng Wang. Synthetic Aperture Radar Image Filtering Based on Clustering Three-Dimensional Block-Matching[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041004.

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