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一种空域和频域相结合的图像消噪方法 被引量:3

Novel method of image denoising combining spatial space and frequency domain
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摘要 提出一种基于非均匀剖分和V变换相结合的图像消噪新方法。非均匀剖分逼近在空域上视像素的灰度值为拟合数据,依据最小二乘法原理,对含噪图像进行非均匀三角剖分,使图像表示为一个分片多项式,它能保持图像的边缘及细节特征,并通过对剖分精度的控制来实现图像的消噪;V系统是一类L2[0,1]空间上的完备正交系,具有多小波的多分辨特性,利用相应的V变换将图像变换到频域,通过对高频低频的系数处理来达到消噪的目的。结合非均匀剖分逼近和V变换两者的优势,将两个方法的消噪结果加权平均,得到一种新的消噪方法。实验结果表明了该方法的消噪效果比很多经典方法更好。 A new images denoising method based on non-uniform partition approximation and V-transform is proposed.The key point of non-uniform partition approximation is that the gray values of the pixels are regarded as fitting data,which are fitted with the least squares method,and then the digital image is expressed as a piecewise polynomial.It can retain the edge as well as detailed characteristics of image.The non-uniform partition approximation can realize image denoising by controlling partition precision value.V-system is a class of complete orthogonal function system on L 2 [0,1],with the characteristics of the multi-resolution of multi-wavelet.It can transform a digital image from spatial space to frequency domain,and by processing the coefficients in high frequency and low frequency to realize image denoising.This paper combines the advantages of these two methods to process image deniosing.The experimental results show that the proposed method is efficient to improve the quality of denoising images.
出处 《计算机工程与应用》 CSCD 北大核心 2011年第34期184-186,211,共4页 Computer Engineering and Applications
基金 国家自然科学基金(No.10631080 No.10771002) 北京市自然科学基金(No.1102017) 澳门科学技术发展基金(No.008/2008/A1)~~
关键词 非均匀剖分 V系统 离散V变换 图像消噪 空域 频域 non-uniformly partition V-system discrete V-transform image denoising spatial space frequency domain
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参考文献16

  • 1[ Shui Penglang.Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain[J]. IEEE Signal Processing Letters, 2005,12 ( 10 ) : 681-684.
  • 2Shui P L, Zhou Z F, Li J X.Image denoising algorithm via best wavelet packet base using Wiener cost function[J].IEEE Image Process, 2007,1 (3) : 311-318.
  • 3Donoho D L, Johnstone I M.Ideal spatial adaptation by wavelet shrinkage[J].Biometrika, 1994,81 (3) : 425-455.
  • 4Donoho D L.Denoising by soft thresholding[J].IEEE Transactions on Information Theory, 1995,41 (3) : 613-627.
  • 5Sudha S,Suresh G R, Sukanesh R.Wavelet based image denoising using adaptive thresholding[C]//Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, 2007: 296-300.
  • 6Luisier F,Blu T.SURE-LET multicharmel image denoising: interscale orthonormal wavelet thresholding[J].IEEE Transaction on Image Processing, 2008,17 (4) : 482-492.
  • 7Zhang W,Wei K, Liu X.lmage clenoising using multiple wavelet representations and local contextual hidden Markov model[C]// Proceedings of the International Conference on Robotics and Biomimetics, 2007:156-161.
  • 8Pirsiavash H, Kasaei S, Marvasti EAn efficient parameter selection criterion for image denoising[C]//Proceedings of IEEE International Symposium on Signal Processing and Information Technology, Athens, Greece, 2005 : 872-877.
  • 9Eslani R, Radha H.Translation-invariant contourlet transform and its application to image denoising[J].IEEE Transactions on Image Processing,2006,15( 11 ) : 3362-3374.
  • 10Cunha A L, Zhou J, Do M N.The nonsubsampled contourlet transform: theory, design, and applications[J].IEEE Transactions on Image Processing,2006,15(10) :3089-3101.

二级参考文献25

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同被引文献24

  • 1戴维,于盛林,孙栓.基于Contourlet变换自适应阈值的图像去噪算法[J].电子学报,2007,35(10):1939-1943. 被引量:53
  • 2Ngan H Y T,Pang G K H,Yung N H C.Automated fabric defect detection—a review[J].Image and Vision Computing,2011,29(2):442-458.
  • 3Fan H.Image denoising algorithm based on dyadic contourlet transform[J].Journal of Software,2011,6(6):1117-1124.
  • 4Sudha S,Suresh G R,Sukanesh R.Wavelet based image denoising using adaptive subband thresholding[J].International Journal of Soft Computing,2007,2(5):628-632.
  • 5Shan Hao,Ma Jianwei,Yang Huizhu.Comparisons of wavelets,contourlets and curvelets in seismic denoising[J].Journal of Applied Geophysics,2009,69:103-115.
  • 6Satheesh S,Prasad D K.Medical image denoising using adaptive threshold based on Contourlet transform[J].Advanced Computing:An International Journal(ACIJ),2011,2(2):52-58.
  • 7Do M N,Vetterli M.The contourlet transform:an efficient directional multiresolution image representation[J].IEEE Transactions on Image Processing,2005,14(12):2091-2106.
  • 8江玉乐,张楠.探地雷达在隧道工程检测中的应用[J].勘察科学技术,2008(1):58-61. 被引量:7
  • 9让晓勇,叶俊勇,郭春华.基于二维经验模态和均值滤波的图像去噪方法[J].计算机应用,2008,28(11):2884-2886. 被引量:6
  • 10蒋蕾,尹业安,常利利.基于计算机视觉的织物疵点自动检测方法研究[J].棉纺织技术,2008,36(11):29-32. 被引量:3

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