Complex Daubechies wavelets: properties and statistical image modelling

被引:38
作者
Clonda, D [1 ]
Lina, JM [1 ]
Goulard, B [1 ]
机构
[1] Univ Montreal, Ctr Rech Math, Network Comp & Math Modeling, Montreal, PQ H3C 3J7, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
complex wavelet; statistical modelling; hidden Markov models; image processing;
D O I
10.1016/j.sigpro.2003.06.001
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article presents the construction and various properties of complex Daubechies wavelets with a special emphasis on symmetric solutions. Such solutions exhibit interesting relationships between the real and imaginary components of the complex scaling function and the complex wavelet. We present those properties in the context of image processing. Within the framework of statistical modelling, we focus on the redundant description of real images given by the complex multiresolution representation. A hierarchical Markovian Graphical model is then explored. We present an Expectation Maximization algorithm for optimizing the model with observational complex wavelet data. This model is then applied to image estimation and texture classification. In both applications, we demonstrate the benefit brought by the Markovian hypothesis and the performance of the real images's complex multiscale representation. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 23
页数:23
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