本文選題：高斯比例混合模型 + 同步稀疏編碼　； 參考：《信號處理》2017年11期

【摘要】：針對SAR圖像降斑過程中會產(chǎn)生過平滑現(xiàn)象及相干斑的濾除不徹底等問題,提出了稀疏結(jié)構(gòu)符合高斯比例混合(Gaussian Scale Mixture,GSM)模型的SAR圖像降斑算法。根據(jù)貝葉斯原理以及相干斑的統(tǒng)計特性推導該算法的數(shù)學模型,在塊匹配過程中使用概率而不是歐式距離進行權(quán)重衡量,根據(jù)圖像塊之間的結(jié)構(gòu)相似度,可以有效區(qū)分同質(zhì)區(qū)與異質(zhì)區(qū),并得到圖像塊的較優(yōu)均值估計。使用PCA字典學習方法對每個圖像塊進行子字典訓練,實現(xiàn)同步稀疏編碼(Simultaneous Sparse Coding,SSC),數(shù)學模型的求解利用迭代正則化方法。分別使用合成場景SAR圖像及真實場景SAR圖像對算法進行驗證,實驗表明,相比于目前已提出的PPB算法、SAR-BM3D算法及FANS算法,該算法能有效提高等效視數(shù),在濾除相干斑的同時很好地保留了圖像的局部結(jié)構(gòu)特性與紋理特征。
[Abstract]:In order to overcome the problems of over-smoothing and incomplete speckle filtering in SAR image, a new algorithm for speckle reduction in SAR image is proposed, which accords with Gao Si's proportional mixing Gaussian Scale mixture (GSM) model. According to the Bayesian principle and the statistical characteristics of speckle, the mathematical model of the algorithm is deduced. In the process of block matching, the weight is measured by probability instead of Euclidean distance. The homogeneous region and the heterogeneous region can be effectively distinguished, and the optimal mean estimation of the image block can be obtained. The PCA dictionary learning method is used to train the sub-dictionary of each image block to realize the synchronous sparse coding Simultaneous Sparse coding and the iterative regularization method is used to solve the mathematical model. The algorithm is verified by synthetic scene SAR image and real scene SAR image respectively. The experimental results show that the algorithm can effectively improve the equivalent visual number compared with the existing PPB algorithm and FANS algorithm. The local structure and texture features of the image are well preserved while the speckle is removed.
【作者單位】： 南京航空航天大學自動化學院;
【基金】：國家自然科學基金項目(61501228)
【分類號】：TN957.52
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本文編號：1803271