利用灰色绝对关联分析的中值滤波方法研究
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摘要
针对灰色图像中的椒盐噪声,提出了一种利用绝对灰色关联度分析方法区分噪声与非噪声,并结合中值滤波进行去噪的算法。将n′n(n为大于或等于3的奇数)模板中的像素组合成两组序列,利用灰色绝对关联分析法,计算出这两组序列的相似关联度,从而判断当前像素是否为噪声。对于被判定为噪声的像素,进行中值滤波处理,以实现去噪;对于非噪声像素则不作处理,从而较好地保留了图像的细节。实验结果表明,与同类方法相比较,此方法有良好的去噪效果。
Against salt-and-pepper noise in gray image, this paper proposes a method to distinguish between noise and non-noise by absolute grey relation degree, and uses median filtering algorithm to denoise the noise. After combining the pixels in n ′ n( n is an odd number greater than or equal to 3)template into two groups, the paper uses absolute grey relation degree analysis algorithm to calculate the similarity of these two pixels groups, then it can know whether the current pixel is noise or not. For the noise pixel, denoise it by median filter;for the non-noise one, do nothing, thus retain good image detail. Experimental results show that, this method has good denoising results compared with similar methods.
Against salt-and-pepper noise in gray image, this paper proposes a method to distinguish between noise and non-noise by absolute grey relation degree, and uses median filtering algorithm to denoise the noise. After combining the pixels in n ′ n( n is an odd number greater than or equal to 3)template into two groups, the paper uses absolute grey relation degree analysis algorithm to calculate the similarity of these two pixels groups, then it can know whether the current pixel is noise or not. For the noise pixel, denoise it by median filter;for the non-noise one, do nothing, thus retain good image detail. Experimental results show that, this method has good denoising results compared with similar methods.
引文
[1]SulaIman S N,Matlsa N A.Denoising-based clustering algorithms for segmentation of low level salt-and-pepper noisecorrupted images[J].IEEE Transaction on Consumer Electronics,2010,56(4):2702-2710.
[2]Ron Z.Enterprise application integration with CORBA[M].[S.l.]:John Wiley and Sons,2000.
[3]Sun T,Neuvo Y.Detail-preserving median based filters in image processing[J].Pattern Recognition Letters,1994,15:341-347.
[4]Wang Z,Zhang D.Progressive switching median filter for the removal of impulse noise from highly corrupted images[J].IEEE Trans on Circuits and Systems-II:Analog and Digital Signal Processing,1999,46(1):78-80.
[5]Han W Y,Lin J C.Minimum-maximum exclusive mean filter to remove impulse noise from highly corrupted images[J].IEEE Electronics Letters,1997,33(2):124-125.
[6]邢藏菊,王守觉,邓浩江,等.一种基于极值中值的新型滤波算法[J].中国图象图形学报,2001,6(6):533-536.
[7]董继扬,张军英.一种简单的椒盐噪声滤波算法[J].计算机工程与应用,2003,39(20):27-28.
[8]宋宇,李满天.基于相似度函数的图像椒盐噪声自适应滤除算法[J].自动化学报,2007,33(5):474-479.
[9]邓聚龙.灰色系统理论教程[M].武汉:华中理工大学出版社,1990.
[10]洪俊田,陶剑锋,李刚.基于灰色关联度的数字图像去噪研究[J].武汉理工大学学报:交通科学与工程版,2006(2):15-17.
[11]李艳玲,黄春艳,赵娟.基于灰色关联度的图像自适应中值滤波算法[J].计算机仿真,2010,27(1):238-240.
[12]梅振国.灰色绝对关联度及其计算方法[J].系统工程,1992,10(5):43-44.
[2]Ron Z.Enterprise application integration with CORBA[M].[S.l.]:John Wiley and Sons,2000.
[3]Sun T,Neuvo Y.Detail-preserving median based filters in image processing[J].Pattern Recognition Letters,1994,15:341-347.
[4]Wang Z,Zhang D.Progressive switching median filter for the removal of impulse noise from highly corrupted images[J].IEEE Trans on Circuits and Systems-II:Analog and Digital Signal Processing,1999,46(1):78-80.
[5]Han W Y,Lin J C.Minimum-maximum exclusive mean filter to remove impulse noise from highly corrupted images[J].IEEE Electronics Letters,1997,33(2):124-125.
[6]邢藏菊,王守觉,邓浩江,等.一种基于极值中值的新型滤波算法[J].中国图象图形学报,2001,6(6):533-536.
[7]董继扬,张军英.一种简单的椒盐噪声滤波算法[J].计算机工程与应用,2003,39(20):27-28.
[8]宋宇,李满天.基于相似度函数的图像椒盐噪声自适应滤除算法[J].自动化学报,2007,33(5):474-479.
[9]邓聚龙.灰色系统理论教程[M].武汉:华中理工大学出版社,1990.
[10]洪俊田,陶剑锋,李刚.基于灰色关联度的数字图像去噪研究[J].武汉理工大学学报:交通科学与工程版,2006(2):15-17.
[11]李艳玲,黄春艳,赵娟.基于灰色关联度的图像自适应中值滤波算法[J].计算机仿真,2010,27(1):238-240.
[12]梅振国.灰色绝对关联度及其计算方法[J].系统工程,1992,10(5):43-44.