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首页> 外文期刊>Journal of Computational Methods in Sciences and Engineering >Speckle noise filtering in SAR images using fuzzy logic and particle swarm optimization
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Speckle noise filtering in SAR images using fuzzy logic and particle swarm optimization

机译：使用模糊逻辑和粒子群优化的SAR图像中的斑点噪声滤波

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摘要

Speckle noises inherently exist in Synthetic Aperture Radar (SAR) image. It reduces the quality of the image and must be removed. Recently, several fuzzy based noise filters have been proposed and it is claimed that they are more superior to the existing state of the art classical filters. However, most of these filters are designed for removing impulse noise. This paper presents a new fuzzy weighted mean filter for SAR images. The weights are computed using fuzzy rules. These rules can differentiate variation in pixel values due to noise and edges. In edge region, the neighboring pixels are assigned with less weight, and thereby preserving the edge pixels. The value of parameters used in defining fuzzy membership functions is determined using Particle Swarm Optimization (PSO) technique. The proposed fuzzy filter is comparatively assessed using Equivalent Number of Looks (ENL), Mean of Ratio (MoR), Signal-to-Noise Ratio (SNR), and Edge-Preservation Factor (EPF) with some of the existing noise removal techniques. It is found that the proposed filter can suppress speckle noise present in SAR images and simultaneously preserve the image details.
机译：合成孔径雷达（SAR）图像中固有地存在斑点噪声。它降低了图像的质量，必须删除。最近，已经提出了几种模糊的基于模糊的噪声滤波器，并且要求它们更优于现有的艺术典型滤波器。但是，大多数这些过滤器都设计用于去除脉冲噪声。本文介绍了SAR图像的新模糊加权平均滤波器。使用模糊规则计算权重。由于噪声和边缘，这些规则可以区分像素值的变化。在边缘区域中，相邻像素被较低的重量分配，从而保持边缘像素。使用粒子群优化（PSO）技术确定定义模糊隶属函数的参数的值。所提出的模糊滤波器使用当量的外观（EL），比率（MOR），信噪比（SNR）和边缘保存因子（EPF）的平均值进行评估，其中一些现有的噪声消除技术。发现所提出的滤波器可以抑制SAR图像中存在的散斑噪声并同时保留图像细节。

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