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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Discontinuity Adaptive Non-Local Means With Importance Sampling Unscented Kalman Filter for De-Speckling SAR Images
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Discontinuity Adaptive Non-Local Means With Importance Sampling Unscented Kalman Filter for De-Speckling SAR Images

机译：具有去噪SAR图像的重要采样无味卡尔曼滤波器的不连续性自适应非局部均值

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摘要

This paper brings out two extensions/utilities of well-known non-local means filter. Firstly, we propose an improvement to basic non-local means filter (NLMF) by incorporating discontinuity adaptive weights, instead of Gaussian weights for similarity assessment of patches in NLMF. As against the NLMF, discontinuity-adaptive NLMF (DA-NLMF) exhibit better edge and detail preserving capability, while providing very effective de-noising at the homogeneous areas. Secondly, we propose to apply DA-NLMF on recently proposed Importance Sampling Unscented Kalman Filter (ISUKF) [1], which can also be thought of as an elegant post processing technique, in general. Specifically, we propose an efficient method for de-speckling Synthetic Aperture Radar imagery by combining ISUKF and DA-NLMF. In our approach, the DA-NLMF provides an efficient de-speckling as well as feature preservation, when its parameters of its (DA) weighting function are derived from ISUKF results. The performance of these methods is demonstrated on both synthetic and real examples, and the proposed method gives excellent results than the standard speckle filtering methods as well as various advanced methods.
机译：本文介绍了两个著名的非本地均值滤波器的扩展/实用程序。首先，我们提出了一种基本非局部均值滤波器（NLMF）的改进方法，它采用了不连续性自适应权重代替高斯权重来评估NLMF中补丁的相似性。与NLMF相比，支持不连续性的NLMF（DA-NLMF）具有更好的边缘和细节保留能力，同时在均匀区域提供了非常有效的降噪功能。其次，我们建议将DA-NLMF应用于最近提出的重要采样无味卡尔曼滤波器（ISUKF）[1]，通常也可以将其视为一种优雅的后处理技术。具体来说，我们提出了一种通过结合ISUKF和DA-NLMF对合成孔径雷达图像进行散斑的有效方法。在我们的方法中，当DA-NLMF（DA）加权函数的参数是从ISUKF结果中得出时，它就可以提供有效的去斑点和特征保留。这些方法的性能在合成实例和实际实例上都得到了证明，与标准的斑点滤波方法以及各种先进方法相比，所提出的方法具有出色的效果。

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