小波域非局部均值无芒隐子草叶切片盲去噪  

Blind Image Denoising of Microscopic Image of Cleistogenes Songorica’s Leaf Based on NL-means Algorithm in Wavelet Domain

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作  者:张文霞[1,2] 王春光 殷晓飞[3] 王海超[1] 王圆[2] 郭华 赵晓宇 Zhang Wenxia;Wang Chunguang;Yin Xiaofei;Wang Haichao;Wang Yuan;Guo Hua;Zhao Xiaoyu(College of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China;Department of Electronic Information Engineering,Ordos Institute of Applied Technology,Ordos 017000,China;College of Mechanical and Electrical engineering,Hohhot Vocational College,Hohhot 010070,China)

机构地区:[1]内蒙古农业大学机电工程学院,呼和浩特010018 [2]鄂尔多斯应用技术学院信息工程学院,内蒙古鄂尔多斯017000 [3]呼和浩特职业学院机电工程学院,呼和浩特010018

出  处:《农机化研究》2019年第10期35-39,共5页Journal of Agricultural Mechanization Research

基  金:教育部'云教融合科教创新'基金项目(2017A10019);内蒙古自治区高等学校研究项目(NJZY070);鄂尔多斯应用技术学院一般项目(KYYB2017004).

摘  要:在获取无芒隐子草叶切片图像时不可避免受到噪声的污染,易导致后续提取和测量特征参数的不准确。对于自然图像,事先并不知道其所含噪声的类型和方差,因而首先利用小波变换和曲线拟合确定切片图像噪声类型和强度;在此基础上,分别应用小波阈值去噪、非局部均值去噪和提出的非局部均值滤波(NLM)与小波阈值去噪相结合的方法对无芒隐子草叶切片图像进行去噪。实验结果表明:获取的切片图像噪声类型为高斯加性噪声,标准差为σ∈[1.5,3.5],用高斯函数对随机选取的10幅切片图像的高频HH子带能量分布进行拟合,拟合优度为R^2=0.990 7;用3种方法对含不同噪声大小的切片图像进行去噪,当噪声标准差为σ∈[1.5,8]时,应用Beyes Shrink法去噪后,图像的峰值信噪比提高了3dB,而NLM和本文提出的算法不适用;当噪声标准差为σ∈[8,15]时,NLM算法和提出的算法去噪效果相当,去噪后图像峰值信噪比提高了7.5dB,应用Beyes Shrink算法提高了6.5dB;而当σ∈[15,30]时,使用提出的算法表现出较大的优越性,去噪后图像峰值信噪比提高了10.53dB,是NLM算法的1.4倍、Beyes Shrink法的1.3倍。本文的算法和实验结论可为无芒隐子草切片图像准确降噪提供理论基础。The microscopic images of Cleistogenes Songorica’s leaf are inevitably corrupted by noise in obtaining,which will be easy to lead to the inaccuracy of its subsequent extraction and measurement of the feature parameters.Wavelet transform and curve fitting are used to determine the type and intensity of the microscopic image noise,in this paper.On this basis,wavelet threshold method,non local algorithm and the proposed combined non local mean filter(NLM)and wavelet threshold method are applied to de noise the slice images.The results show that:The noise type was recognized as additive Gaussian noise.Energy distribution of high frequency HH sub-band of 10 microscopic images randomly selected were fitted with the Gaussian function,with the R^2 value of 0.990 7.And the standard deviation of the noise was estimatedσ∈[1.5,3.5].The peak signal to noise ratio of the image after denoising by Beyes Shrink method is improved by 3dB when the noise standard deviationσ∈[1.5,3.5],while NLM and the algorithm proposed in this paper are useless.whenσ∈[8,15],NLM and the algorithm in this paper has equal quality,The peak signal to noise ratio of the image after denoising is improved by 7.5dB but using Beyes Shrink,it is only improved by 6.5dB;whenσ∈[15,30],The peak signal to noise ratio of the image after denoising by the algorithm proposed in this paper is improved by 10.53dB,which is 1.4 times as much as the NLM algorithm and 1.3 times as much as the Beyes Shrink algorithm.The algorithm proposed in this paper shows great superiority.The conclusion of this paper provides theoretical guidance and technical support for the accurate noise reduction of the slice images of Cleistogenes Songorica’s leaf.

关 键 词:小波变换 无芒隐子草 噪声识别 图像去噪 非局部均值 

分 类 号:TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]

 

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