Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ${\ell _{1}}$ -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ${\ell _{1}}$ -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called Fisher–Tippett (FT) distribution- ${\ell _{p}}$ -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method.

中文翻译：

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基于非凸混合总变分模型的SAR图像散斑减少

合成孔径雷达（SAR）图像固有的散斑噪声严重影响视觉效果，给SAR图像的后处理带来很大困难。由于边缘保留特性，基于总变异 (TV) 正则化的技术已被广泛用于减少散斑。然而，辐射度比周围区域大几个数量级的 SAR 图像中的强散射限制了 TV 正则化的有效性。与此同时， ${\ell _{1}}$ -norm 一阶 TV 正则化有时会导致阶梯伪影，因为它有利于分段常数的解决方案，并且它通常会低估图像梯度的高振幅分量，因为 ${\ell _{1}}$ -norm 统一惩罚幅度。为了克服这些缺点，一种新的混合变异模型，称为 Fisher-Tippett (FT) 分布 - ${\ell _{p}}$ -norm 一阶和二阶混合电视 (HTpVs) 被提议用于在去除强散射后减少散斑。特别是 FT-HTpV 继承了基于分布的数据保真度项、非凸正则化和高阶 TV 正则化的优点。因此，它可以有效去除散斑，同时保留点散射和边缘，并很好地减少阶梯伪影。为了有效地解决非凸最小化问题，使用了具有非单调加速近端梯度 (nMAPG) 方法和矩阵向量加速策略的迭代框架。在模拟和真实 SAR 图像上的大量实验证明了所提出方法的有效性。