基于图像受噪程度的改进模糊加权均值滤波

张鲁丹

基于图像受噪程度的改进模糊加权均值滤波

引用本文：张鲁丹.基于图像受噪程度的改进模糊加权均值滤波[J].计算技术与自动化,2016,(2):66-70

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作者	单位
张鲁丹	(渤海大学工学院自动化系，辽宁锦州121013)

中文摘要:根据高斯噪声密度大、噪声强度的波动范围宽，其污染图像不仅每一个像素灰度级都会受影响，而且即使是同一灰度级受污染的程度也会不同的特点和传统的图像模糊滤波算法在图像细节保护方面上的不足，提出基于图像受噪程度的改进模糊加权均值滤波算法，该算法根据图像各像素点的受噪程度，得到首次滤波图像和原图像估计直方图，根据该直方图确定模糊隶属度函数，然后对首次滤波图像中灰度小于25的像素点进行模糊加权均值滤波，该算法在不需要期望图像和高斯噪声方差的情况下能有效地去除噪声，同时能够很好地保护图像细节信息。

中文关键词:高斯噪声模糊滤波直方图

Improving Fuzzy Weighted Mean Filter Based on the Degree of Image Noise

Abstract:The density of Gaussian noise and the wide range of noise intensity fluctuation make its polluted images not only every pixel gray scale affected, but also the same grayscale contaminated degree different. Aiming at the defects of the traditional filter algorithm on the image detail protection, an improved fuzzy weighted mean filter algorithm based on image pixel level of noise was proposed. At first, the algorithm based on image pixel level of noise gets the first time filtering image and the original image estimated histogram. The histogram determines the fuzzy membership function, and the fuzzy weighted mean filter is used to filter those pixels in the first time filtering image whose gray scale values are less than 25. The algorithm under the condition without Gaussian noise variance and except image can effectively remove the noise and protect the image detail information.

keywords:gaussian noise fuzzy filter histogram

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