基于非凸加权范数约束的SAR图像降斑

结合相似图像块具有低秩的特性提出了一种非凸加权范数约束(non-convex weighted norm constrain, NWNC)的合成孔径雷达(synthetic aperture radar, SAR)图像降斑方法。首先对每个目标块寻找相似图像块构建相似图像块集合;然后对相似图像块集合的系数矩阵进行NWNC;再利用广义阈值收缩法估计系数矩阵;最后对系数矩阵进行反变换重构出降斑图像。实验结果表明,该方法不仅有效地解决了传统低秩核范数约束不足的问题,而且通过NWNC和广义阈值收缩估计系数使得系数估计更加精确,表现在抑制斑点噪声的同时可以很好地保护图像的纹理细节。

SAR image despeckling based on non-convex weighted norm constraint

The non-convex weighted norm constrained (NWNC) synthetic aperture radar (SAR) image despeckling method is proposed based on the low rank property of similar image patch sets. Firstly, some similar image patches are searched for each target patch to construct a set of similar image patches. Secondly, NWNC is applied to the coefficient matrix. Then the coefficient matrix is estimated by generalized threshold shrinkage. Finally, the despeckled image is reconstructed by the inverse transformation of the coefficient matrix. The experimental results show that NWNC not only effectively solves the problem of insufficient constraints of traditional low-rank kernel norms, but also makes the coefficients estimation more accurate through NWNC and generalized threshold shrinkage, which can suppress speckle noise and protect the texture details of the image well.