[1]黄玲俐. 一种改进权重的非局部均值图像去噪方法[J].计算机技术与发展,2016,26(06):16-19.
　HUANG Ling-li. A Non-local Means Denoising Algorithm with Improved Weighted Function[J].,2016,26(06):16-19.
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 一种改进权重的非局部均值图像去噪方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年06期
页码:
16-19
栏目:
智能、算法、系统工程
出版日期:
2016-06-10

文章信息/Info

Title:
 A Non-local Means Denoising Algorithm with Improved Weighted Function
文章编号:
1673-629X（2016)06-0016-04
作者:
 黄玲俐
 电子科技大学 数学科学学院
Author(s):
 HUANG Ling-li
关键词:
 ; 图像去噪; 非局部均值去噪; 加权核函数; 高斯噪声
Keywords:
 ; image denoising; non-local means denoising; weighted kernel function; Gaussian noise
分类号:
TP301
文献标志码:
A
摘要:
 非局部均值( Non-Local Means,NLM)去噪采用图像邻域间的自相似性构造权重,进而达到图像恢复的效果。文中对非局部均值去噪模型进行了介绍说明,尤其是对原始非局部均值去噪算法中的核函数—指数函数进行了描述,并且通过对几种新的加权核函数的分析说明,综合几种的优缺点,提出了一种新的加权核函数。然后又对双边滤波算法进行了研究说明,借鉴双边滤波的优点,再结合之前提出的新的加权核函数,进而得到了一种改进的权重函数,提出了一种新的权重计算公式,得到了一种改进的非局部均值去噪算法。通过对添加不同噪声水平的噪声图像进行实验,结果表明,与传统的非局部均值滤波算法相比,文中算法保护了恢复图像的边缘,突出了几何特征和纹理,去噪效果比原有算法有所提高,在去噪性能和结构信息上均有显著效果。
Abstract:
 The NLM denoising uses self-similarity of image between neighborhood to construct weight,thus to achieve the effect of image restoration. The non-local means denoising model is introduced in this paper,especially for the exponential function which is the kernel function in the original non-local means denoising algorithm. And through the analysis of several new weighted kernel function,integrat-ed the advantages and disadvantages of them,a new weighted kernel function is put forward. Then research on the bilateral filtering algo-rithm,reference of its advantages,and combined with new previous kernel function,an improved weighted function is obtained,proposing a new formula of weight,getting an improved non-local means denoising algorithm. The proposed method has been evaluated on testing images with various levels noise. Numerical results show that compared with the traditional non-local means algorithm, the improved method can protect the edges,highlight the geometry features and texture,make the denoising image become more clear and result in a better effect. The proposed method improves the denoising performance as well as the preservation of structure information.

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更新日期/Last Update: 2016-09-19