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Noise Reduction by Wavelet Thresholding

  • Book
  • © 2001

Overview

Authors:
  1. Maarten Jansen

    1. Departement Computerwetenschappen, Katholieke Universiteit Leuven, Heverlee, Belgium

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Part of the book series: Lecture Notes in Statistics (LNS, volume 161)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xx
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  2. Introduction and overview

    • Maarten Jansen
    Pages 1-7

  3. Wavelets and wavelet thresholding

    • Maarten Jansen
    Pages 9-45

  4. The minimum mean squared error threshold

    • Maarten Jansen
    Pages 47-79

  5. Estimating the minimum MSE threshold

    • Maarten Jansen
    Pages 81-100

  6. Thresholding and GCV applicability in more realistic situations

    • Maarten Jansen
    Pages 101-138

  7. Bayesian correction with geometrical priors for image noise reduction

    • Maarten Jansen
    Pages 139-160

  8. Smoothing non-equidistantly spaced data using second generation wavelets and thresholding

    • Maarten Jansen
    Pages 161-175

  9. Back Matter

    Pages 177-194
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Keywords

About this book

Wavelet methods have become a widely spread tool in signal and image process­ ing tasks. This book deals with statistical applications, especially wavelet based smoothing. The methods described in this text are examples of non-linear and non­ parametric curve fitting. The book aims to contribute to the field both among statis­ ticians and in the application oriented world (including but not limited to signals and images). Although it also contains extensive analyses of some existing methods, it has no intention whatsoever to be a complete overview of the field: the text would show too much bias towards my own algorithms. I rather present new material and own insights in the questions involved with wavelet based noise reduction. On the other hand, the presented material does cover a whole range of methodologies, and in that sense, the book may serve as an introduction into the domain of wavelet smoothing. Throughout the text, three main properties show up ever again: sparsity, locality and multiresolution. Nearly all wavelet based methods exploit at least one of these properties in some or the other way. These notes present research results of the Belgian Programme on Interuniver­ sity Poles of Attraction, initiated by the Belgian State, Prime Minister's Office for Science, Technology and Culture. The scientific responsibility rests with me. My research was financed by a grant (1995 - 1999) from the Flemish Institute for the Promotion of Scientific and Technological Research in the Industry (IWT).

Authors and Affiliations

  • Departement Computerwetenschappen, Katholieke Universiteit Leuven, Heverlee, Belgium

    Maarten Jansen

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