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High density impulse noise removal by Fuzzy Mean Linear Aliasing Window Kernel

机译:用模糊平均线性别名窗口核去除高密度脉冲噪声

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Fuzzy Mean Linear Aliasing Window Kernel (FMLAWK) filter method proposed to reducing the high-density impulse noise interference and generating the smooth image performance. FMLAWK filter is a spatial filter, which combined from fuzzy method and Linear Aliasing Filter (LAF). The initial step is finding the degree of membership function (μ) value of each matrix element on the corrupted image which use the fuzzy method. Furthermore, the μ value of the corrupted image processed by LAF method which using 3×3 window. The reducing of 3×3 windows on LAF process will be obtain one pixel data based on Linear method. Our research also provides kernel algorithms. Preprocessing Kernel algorithm used for checking of each element matrix on the 3×3 window. If the matrix element contaminated by impulse noise, so the matrix element replaced with a new element data. Our simulation result shows the image filtering better and smoother quality than the comparison method.
机译:提出了一种模糊平均线性别名窗口核(FMLAWK)滤波方法,以减少高密度脉冲噪声干扰并产生平滑的图像性能。 FMLAWK滤波器是一种空间滤波器,它是由模糊方法和线性别名滤波器(LAF)组合而成的。第一步是使用模糊方法找到损坏图像上每个矩阵元素的隶属度(μ)值。此外,使用3×3窗口通过LAF方法处理的损坏图像的μ值。在LAF过程中减少3×3窗口将基于线性方法获得一个像素数据。我们的研究还提供了内核算法。预处理内核算法,用于检查3×3窗口中的每个元素矩阵。如果矩阵元素被脉冲噪声污染,那么矩阵元素将替换为新的元素数据。我们的仿真结果表明,与比较方法相比,图像滤波具有更好,更平滑的质量。

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