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小波域图像降噪概述

DOI: 10.11834/jig.200609203

Keywords: 小波域图像降噪,贝叶斯准则,先验概率,小波系数建模

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Abstract:

小波域图像降噪是图像处理中一个引人注目的研究方向,为了使人们对小波域图像降噪有一概括了解,在对小波域图像降噪相关文献进行分析和理解的前提下,首先给出了小波变换的特性,同时阐述了小波去噪的最优准则和对图像进行小波变换时小波基的选取原则,然后评述了用于图像降噪的方法,并分析了利用小波系数建模的常用方法,最后探讨了小波域图像降噪的发展方向。

References

[1]  Feng Zhi-lin,Yin Jian-wei,Liu Yang,et al.Research on jacquard fabrics image denoising using Allen-Cahn level set model[J].Journal of Zhejiang University:Engineering Science,2005,39(2):185~189.[冯志林,尹建伟,刘洋等.Allen-Cahn水平集的提花织物图像去噪研究[J].浙江大学学报,2005,39(2):185~189.]
[2]  Besag J.Spatial interaction and statistical analysis of lattic systems[J].Journal of Royal Statistical Society,1974,36(2):192~236.
[3]  Chang S G,Yu B,Vetterli M.Adaptive wavelet thresholding for image denoising and compression[J].IEEE Transactions on Image Processing,2000,9(9):1532~1546.
[4]  Mallat S,Hwang W L.Singularity detection and processing with wavelets[J].IEEE Transactions on Information Theory,1992,38(2):617~643.
[5]  Donoho D L,Johnstone Iain M.Adapting to unknown smoothness via wavelet shrinkage[J].Journal of the America Statistical Association,1995,90(432):1200~1224.
[6]  Donoho D,Johnstone Iain M.Ideal spatial adaptation by wavelet shrinkage[J].Biometrika,1994,81(3):425~455.
[7]  Malfait M,Roose D.Wavelet-based image denoising using a Markov random field a priori model[J].IEEE Transactions on Image Processing,1997,6(4):549~565.
[8]  Peng Yu-hua.Wavelet transform and engineering application[M].Beijing:Science Publishing House,2000.[彭玉华.小波变换与工程应用[M].北京:科学出版社,2000.]
[9]  Mallat S G.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674~693.
[10]  Canny J.A computational approach to edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679~698.
[11]  Xu Yan-sun,Weaver J B,Healy D M,et al.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Transactions on Image Processing,1994,3(6):747~758.
[12]  Nason G.Wavelet shrinkage using cross-validation[J].Journal of the Royal Statistical Society,1996,58(6):463~479.
[13]  Wan F Y,Yuan Z D.Optimization problem of shrinkage and thresholding policies in wavelet and the application to image processing[A].In:Proceedings of the 3rd World Congress on Intelligence Control and Automation[C],Hefei China,2000,4:2661~2665.
[14]  Feng Xing-chu,Gan Xiao-bin,Song Gao-xiang.Numerical function and wavelet theory[M].Xi\'an:Xidian University Publishing House,2003.[冯象初,甘小冰,宋国乡.数值泛函与小波理论[M].西安:西安电子科技大学出版社,2003.]
[15]  Sendur Levent,Selesnick Iva W.Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J].IEEE Transactions on Signal Processing,2002,50(11):2744~2756.
[16]  Pizurica A,Philips W.Estimating probability of presence of a signal of interest in multiresolution single-and multiband image denoising[EB/OL].http://telin.rug.ac.be/~sanja /Sanja files/Publications.htm,2005-5-5.
[17]  Zhuang X H,Huang Y,Palaniappan K,et al.Gaussian mixture density modeling,decomposition and applications[J].IEEE Transactions on Image Processing,1996,5(9):1293~1302.
[18]  Xie Hua,Pierce L E,Ulaby F T.SAR speckle reduction using wavelet denoising and Markov random field modeling[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(10):2196~2212.
[19]  Mario A T.Figueiredo,Robert D.Nowak.Wavelet-Based image estimation:an empirical bayes approach using jeffrey\'s noninformative prior[J].IEEE Transactions on Image Processing,2001,10(9):1322~1331.
[20]  Li S Z.Markov random field modeling in computer vision[M].Tokyo,Japan:Springer-Verlag,1995.
[21]  Gidas B.A renormalization group approach to image processing problems[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(2):164~180.
[22]  Fan Guo-liang,Xia Xiang-gen.Image denoising using a local contextual hidden markov model in the wavelet domain[J].IEEE Transactions on Signal Processing Letters,2001,8(5):125~128.
[23]  Pizurica A,Philips W,Lemahieu I,et al.A joint inter-and intrascale statistical model for Bayesian wavelet based image denoising[J].IEEE Transactions on Image Processing,2002,11(5):545~557.
[24]  Chipman H,Kolaczyk E,McCulloch R.Adaptive Bayesian wavelet shrinkage[J].Journal of American Statistical Association,1997,92(440):1413~1421.
[25]  Liu J,Moulin P.Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients[J].IEEE Transactions on Image Processing,2001,10(11):1647~1658.
[26]  Crouse M S,Nowak R D,Baraniuk R G.Wavelet-based statistical signal processing using hidden markov models[J].IEEE Transactions on Signal Processing,1998,46(4):886~902.
[27]  Crouse M S,Baraniuk R G.Contextual hidden Markov models for wavelet-domain signal processing[A].In:Proceedings of the 31th Asilomar Conference Signals,Systems,and Computers[C],Pacific Grove,CA,USA,1997,1(1):95~100.
[28]  Lei Tian-hu,Udupa Jayaram K.Performance evaluation of finite normal mixture model-based image segmentation techniques[J].IEEE Transactions on Image Processing,2003,12(10):1153~1169.
[29]  Wang D,Haese-Coat V,Bruno A,et al.Some statistical properties of mathematical morphology[J].IEEE Transactions on Signal Processing,1995,43(8):1955~1965.
[30]  Besag J.On the statistical analysis of dirty pictures[J].Journal of Royal Statistical Society,1986,48(3):259~302.
[31]  Shapiro J M.Embedded image coding using zerotrees of wavelet coefficients[J].IEEE Transactions on Signal Processing,1993,41(12):3445~3462.
[32]  Mallat S,Zhong S.Characterization of signals from multiscale edges[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(7):710~732.
[33]  Donoho D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613~627.
[34]  Pan Q,Zhang L,Dai G,et al.Two denoising methods by wavelet transform[J].IEEE Transactions on Signal Processing,1999,47(12):3401~3406.
[35]  Crouse M S,Nowak R D,Baraniuk R G.Wavelet based statistical signal processing using hidden Markov models[J].IEEE Transactions on Signal Processing,1998,46(4):886~902.
[36]  Mallat S G.Multifrequency channel decomposition of images and wavelet models[J].IEEE Transactions Acoustics,Speech,Signal Processing,1989,37(12):2091~2110.
[37]  Liu Wei,Ma Zheng-ming.Wavelet image threshold denoising based on edge detection[J].Journal of Image and Graphics,2002,7(8):788~793.[柳薇,马争鸣.基于边缘检测的图象小波阈值去噪方法[J].中国图象图形学报,2002,7(8):788~793.]
[38]  更多...
[39]  Hsung T C,Lun D P K,Siu W C.Denoising by singularity detection[J].IEEE Transactions on Signal Processing,1999,47(11):3139~3144.
[40]  Chang S G,Yu B,Vetterli M.Spatially adaptive wavelet thresholding with context modeling for image denoising[J].IEEE TransactionS on Image Processing,2000,9(9):1522~1531.
[41]  Weyrich N,Warhola G T.Wavelet shrinkage and generalized cross validation for image denoising[J].IEEE Transactions on Image Processing,1998,7(1):82~90.
[42]  Vidakovic B.Nonlinear wavelet shrinkage with bayes rules and bayes factors[J].Journal of the American Statistical Association,1998,93(441):173~179.
[43]  Deng Da-xin,Lin Chun-sheng,Gong Shen-guang,et al.Wavelet threshold denoising method based on neyman-pearson criterion[J].Signal Processing,2003,19(3):281~283.[邓大新,林春生,龚沈光等.基于Neyman-Pearson准则的小波阈值去噪法[J].信号处理,2003,19(3):281~283.]
[44]  Moulin P,Liu J.Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors[J].IEEE Transactions on Information Theory,1999,45(3):909~919.
[45]  Achim A,Tsakalides P,Beserianos A.SAR Image denoising via Bayesian wavelet shrinkage based on heavy tailed modeling[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(8):1773~1784.
[46]  Portilla Javier,Strela Vasily,Wainwright Martin J,et al.Image denoising using scale mixtures of Gaussians in the wavelet domain[J].IEEE Transactions on Image Processing,2003,12(11):1338~1351.
[47]  Geman S,Geman D.Stochastic relaxation,Gibbs distribution,and the Bayesian restoration of images[J].IEEE Transaction on Pattern Analysis Machine Intelligence,1984,6(6):721~741.
[48]  Jansen M,Bultheel A.Empirical bayes approach to improve wavelet thresholding for image noise reduction[J].Journal of the American Statistical Association,2001,96(454):629~639.
[49]  Pesquet J C,Krim H,Hamman E.Bayesian approach to best basis selection[A].In:IEEE International conference on Acoustics,Speech,and Signal Processing(ICASSP-96)[C],Atlanta,GA,USA,1996,5:2634~2637.
[50]  Rabiner L R.A tutorial on hidden Markov models and selected applications in speech recognition[J].IEEE Transactions on Digital Object Identifier,1989,77(2):257~286.
[51]  Zhang Lei,Bao Paul,Wu Xiao-lin.Multiscale lmmse-based image denoising with optimal wavelet selection[J].IEEE Transactions on Circuits and Systems for Video Technology,2005,15(4):469~481.
[52]  Fan Guo-liang,Xia Xiang-gen.Improved hidden markov models in the wavelet domain[J].IEEE Transactions on Signal Processing,2001,49(1):115~120.

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