提出一种基于稀疏优化模型的SAR图像滤波算法。该算法建立在超完备字典稀疏表示基础上,具有较强的数据稀疏性和稳健的建模假设。首先依据SAR图像的结构特征,运用正则化方法建立多目标稀疏优化模型,然后通过冗余字典稀疏优化变换系数,利用冗余字典以及具有点奇异性的小波和线奇异性的剪切波构造超完备字典,最后通过对优化问题的求解,重建SAR图像场景分辨单元的平均强度,实现了SAR图像的滤波。实验结果表明,该算法对SAR图像相干斑噪声具有很好的抑制效果,并且具有增强滤波图像纹理细节特征的优点。

In this paper,a new method for filtering SAR images using sparse optimization model is proposed. The algorithm based on sparse representation via an over-complete dictionary has a strong data sparseness and provides solid modeling assumptions for the data sets. First,a sparse optimization model based on structural properties of then SAR image is built by regulation. Second,a practical optimization strategy is used to design a redundancy dictionary. Then,an over-complete dictionary is constructed by employing a combined dictionary consisting of wavelets,shearlets,and a redundancy dictionary. Finally,the filtering process is realized through the solution of the multi-objective optimization problem in which the mean backscatter power is reconstructed. The experimental results demonstrate that the proposed algorithm has good de-speckling capability and better enhances image details.