采用GSM模型进行稀疏表示的SAR图像降斑算法  

SAR Despeckling Algorithm Based on Sparse Structure and Gaussian Scale Mixture Model

作　　者：何碧容[1] 蔡倩[1] 孔莹莹 周建江 

机构地区：[1]南京航空航天大学自动化学院,江苏南京210016

出　　处：《信号处理》2017年第11期1457-1467,共11页Journal of Signal Processing

基　　金：国家自然科学基金项目(61501228)

摘　　要：针对SAR图像降斑过程中会产生过平滑现象及相干斑的滤除不彻底等问题,提出了稀疏结构符合高斯比例混合(Gaussian Scale Mixture,GSM)模型的SAR图像降斑算法。根据贝叶斯原理以及相干斑的统计特性推导该算法的数学模型,在块匹配过程中使用概率而不是欧式距离进行权重衡量,根据图像块之间的结构相似度,可以有效区分同质区与异质区,并得到图像块的较优均值估计。使用PCA字典学习方法对每个图像块进行子字典训练,实现同步稀疏编码(Simultaneous Sparse Coding,SSC),数学模型的求解利用迭代正则化方法。分别使用合成场景SAR图像及真实场景SAR图像对算法进行验证,实验表明,相比于目前已提出的PPB算法、SAR-BM3D算法及FANS算法,该算法能有效提高等效视数,在滤除相干斑的同时很好地保留了图像的局部结构特性与纹理特征。To overcome the artifacts and incomplete filtering in the denoising process of SAR images, a SAR despeckling algorithm where the sparse structure meets Gaussian Scale Mixture （GSM） model is proposed. The mathematic model derivation of the algorithm is based on Bayes principle and the statistical property of speckle noise. We use the probability rather than the Euclidean distance to measure the weight in the block matching process; by utilizing the structural similarities of the image blocks, we can classify the homogeneous region and heterogeneous area efficiently and get a better mean estimated value of the block. Also, the PCA dictionary learning method is used to train the subdictionary of each image patch, which achieves the Simultaneous Sparse Coding （SSC） process. The model is solved via iteration and regularization, and a local optimized solution of the denoised image matrix is obtained. The algorithm is verified with both simulated SAR images and real SAR images, and the experimental results demonstrate that the proposed approach can improve ENL effectively, and has a competitive performance in terms of despeckling and the protection of local structural characteristics and textural features, when compared with the state-of-the-art despeckling algorithm.

关 键 词：高斯比例混合模型 同步稀疏编码 SAR图像降斑 贝叶斯估计 

分 类 号：TN911.73[电子电信—通信与信息系统]