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基于小波变换的图像去噪方法的研究
引用本文:叶鸿瑾,李祥生,满晰,刘红耀.基于小波变换的图像去噪方法的研究[J].数学的实践与认识,2009,39(23).
作者姓名:叶鸿瑾  李祥生  满晰  刘红耀
作者单位:1. 山西医科大学计算机教学部,山西,太原,030001
2. 山西医科大学第一医院泌尿外科,山西,太原,030001
摘    要:小波变换能有效的去除高斯噪声,中值滤波能有效的去除脉冲噪声,两者结合可以更有效的去除高斯噪声和脉冲噪声的混合噪声.当医学图像去除混合噪声时,先进行中值滤波再进行小波去噪的方法优于先进行小波去噪后再进行中值滤波的方法,且去噪后图像视觉效果较好,而且图像均方误差(M SE)也较小.在图像去噪处理中这种方法具有实际应用价值.

关 键 词:小波变换  中值滤波  图像去噪

Research on Image Denoising Based on Wavelet Transform
YE Hong-jin,LI Xiang-sheng,MAN Xi,LIU Hong-yao.Research on Image Denoising Based on Wavelet Transform[J].Mathematics in Practice and Theory,2009,39(23).
Authors:YE Hong-jin  LI Xiang-sheng  MAN Xi  LIU Hong-yao
Abstract:Wavelet transform can reduce Gaussian noise effectively and median filtering can reduce impulse noise effectively. The combine of wavelet transform and median filtering can reduce mixed noise of Gaussian and impulse more effectively. When medical image with mixed noise is denoised, the way that median filtering is firstly used is better than one that wavelet transform is firstly used. And effect of image vision is good, moreover mean-square error { MSE } is small too. The method has the actual application value in image denoising processing.
Keywords:wavelet transform  median filtering  image denoising
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