The presence of speckle, a granular structure, seriously affects the visual interpretability of the synthetic aperture radar (SAR) images. Recently, model-based reconstruction techniques that use alternating direction method of multipliers (ADMM) have been widely utilised for denoising problems. Owing to the modular structure of ADMM, it is very simple to implement and also enables one to plug in any off-the-shelf denoising algorithm. However, the major limitations of these methods are mostly they have been established for Gaussian noise and the selection of a regulariser for a specific forward model is unknown. In this study, the despeckling of SAR images is addressed in the presence of multiplicative noise such as Rayleigh and Poisson. A mean filter is introduced for images corrupted by Rayleigh-based speckle, which transforms it to Nakagami distributed speckle, to improve the performance. Maximum a posteriori-based estimation involving Nakagami and Poisson distributions are applied to plug-and-play ADMM (PnP ADMM) framework, which provides enhanced despeckling ability along with the preservation of important details. The convergence analysis of the proposed PnP ADMM for multiplicative noise is also provided. The simulation studies are carried out taking several parameters for visual quality and statistical assessment to justify the effectiveness of the proposed method.

中文翻译：

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使用即插即用ADMM进行SAR图像去斑

斑点（颗粒结构）的存在严重影响了合成孔径雷达（SAR）图像的视觉解释性。最近，使用乘数交替方向方法（ADMM）的基于模型的重建技术已被广泛用于消噪问题。由于ADMM的模块化结构，它的实现非常简单，并且还可以插入任何现成的去噪算法。然而，这些方法的主要局限性在于它们大多是针对高斯噪声而建立的，并且对于特定正向模型的正则化器的选择尚不清楚。在这项研究中，SAR图像的散斑问题在存在瑞利和泊松等乘法噪声的情况下得以解决。引入了均值滤波器，以处理基于瑞利散斑的图像，将其转换为中神斑纹，以提高性能。即插即用ADMM（PnP ADMM）框架将涉及Nakagami和Poisson分布的基于后验的估计最大化，该框架提供增强的去斑点能力以及重要细节的保存。还提供了针对乘性噪声的拟议PnP ADMM的收敛性分析。进行了仿真研究，采用了几个视觉质量和统计评估参数来证明所提出方法的有效性。还提供了针对乘性噪声的拟议PnP ADMM的收敛性分析。进行了仿真研究，采用了几个视觉质量和统计评估参数来证明所提出方法的有效性。还提供了针对乘性噪声的拟议PnP ADMM的收敛性分析。进行了仿真研究，采用了几个视觉质量和统计评估参数来证明所提出方法的有效性。