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具有无边界失真的多小波 被引量：5

A MULTIWAVELET WITH NON-BOUNDARY DISTORTION
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摘要 多小波是近几年小波理论研究的一个重要方向。该文综述了多小波的重要性质,利用正交性和对称性构造了一个支集在[0,1]上具有精确重构特性和二阶逼近性的多小波,其最大特点是无边界失真效应;经平衡后,有更好的低通和高通特性,不用预滤波。实验结果表明重构效果比单小波好。 The multiwavelet research has been an important aspect of the wavelet theory in recent years. This paper summarizes some important properties of multiwavelets. Using the properties of orthogonality and symmetry, A multiwavelet with compact support in [0,1], accurate reconstruction and approximation order 2 is constructed. The multiwavelet has the most advantage of non- boundary distortion. Needless to prefilter, it has better lowpass and highpass characteristics after being balanced. Examples of signal reconstruction and image compression are given, with satisfactory reconstruction results over the single wavelet.
作者 王玲 宋国乡
机构地区 西安电子科技大学应用数学系
出处 《电子与信息学报》 EI CSCD 北大核心 2001年第7期693-699,共7页 Journal of Electronics & Information Technology
关键词 多小波 精确重构 边界失真 平衡小波 Multiwavelet, Accurate reconstruction, Boundary distortion, Balanced multi- wavelet
分类号 O177.6 [理学—基础数学]
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参考文献8

  • 1[1]Jiang Qingtang, On the design of multifilter banks and orthonormal multiwavelet banks, IEEE Trans. on Signal Processing, 1998, SP-46(12), 3292-3302.
  • 2[2]T.N.T. Goodman, S. L. Lee, Wavelets of multiplicity r, Trans. Amer. Math. Soc., 1994, 342(1), 307-324.
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  • 5[5]C.K. Chui, J-a. Lian, A study of orthonormal multi-wavelets, Appl. Numer. Math., 1996, 20(3),273-298.
  • 6[6]X.G. Xia, A new prefilter design for discrete multiwavelet transforms, IEEE Trans. on SignalProcessing, 1998, SP-46(6), 1558-1570.
  • 7[7]T.D. Bui, Chen G, Translation-invariant denoising using multiwavelets, IEEE Trans. on SignalProcesying, 1998, SP-46(12), 3414-3420.
  • 8[8]J. Lebrun, M. Vetterli, Balanced multiwavelets theory and design, IEEE Trans. on Signal Pro-cessing, 1998, SP-46(4), 1119-1125.

同被引文献23

  • 1陶闯,林宗坚,卢健.分形地形模拟[J].计算机辅助设计与图形学学报,1996,8(3):178-186. 被引量：26
  • 2Donoho D L. De-noising via soft thresholding. Technical report 409[R]. California:Stanford University, 1992.
  • 3Donoho D L, Johnstone I M. Ideal spatial adaptation via wavelet shrinkage[J]. Biometrika, 1994,81:425-455.
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  • 6Chang S G, Bin Yu, Martin Vetterli. Spatially adaptive wavelet thresholding with context modeling for image denoising[J].IEEE Trans on Image Processing, 2000,9(9) :1522-1531.
  • 7Donoho D L, Johnstone I M. Wavelet shrinkage., asymptopia?[J]. Journal of the Royal Statistical Society, Series B, 1995,57(2) :301-369.
  • 8Mallat S G. A theory for multiresolution signal decomposition:the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intellegence, 1989,11(7) : 674-693.
  • 9Hall P, Koch I. On the feasibility of cross-validation in image analysis [J]. SIAM J Appl Math, 1992, 52(1):292-313.
  • 10Maarten Jansen, Geert Uytterhoeven, Adhemar Buhheel. Image denosing by integer wavelet transforms and generalized cross validation. Technical report TW264 [R]. Belgium:Katholieke Universiteit, 1997.

引证文献5

二级引证文献15

电子与信息学报
2001年 第7期

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