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Sparse Image and Signal Processing
Wavelets, Curvelets, Morphological Diversity

$72.00 ( ) USD

  • Date Published: May 2010
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact for availability.
  • format: Adobe eBook Reader
  • isbn: 9780511731280

$ 72.00 USD ( )
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About the Authors
  • This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research available for download at the associated Web site.

    • Cutting-edge applications are discussed in terms of the algorithms needed, for example, in painting to reconstruct large areas of missing data in images
    • Nearly everything covered in the book can be redone by the reader, using software code from the book's associated web site,
    • Multiresolution and multiscale transforms in image and signal processing need a book like this, providing a central and very accessible source of reference
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    Reviews & endorsements

    "This book is well organized, and it covers the theory and application of multiscale imaging and image processing. The authors provide Matlab algorithms for wavelet, ridgelet and curvelet transformations, as well as numerical experiments with detailed Matlab and IDL code for each chapter. A detailed list of the references provides further exploration of recent publications in the area.
    The book's contents are well prepared for graduate-level students or advanced undergraduates who work in the field of image and signal processing or computer science. The book is also an indispensable resource for professionals looking to adopt innovative concepts for improving the performance of image processing."
    Yan Gao, Osterhout Design Group for Optics & Photonics News

    "This is an excellent book devoted to an important domain of contemporary science, where the activity includes the following stages: research, theory and publication and the verification of results by the scientific community."
    D. Stanomir, Mathematical Reviews

    "The book is effective in delivering a concise overview of the field. The book can be highly useful for researchers and graduate students in engineering and science who are looking for research ideas or are interested in applying the techniques to their application domains."
    T. Kubota, Computing Reviews

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    Product details

    • Date Published: May 2010
    • format: Adobe eBook Reader
    • isbn: 9780511731280
    • contains: 137 b/w illus. 6 tables
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact for availability.
  • Table of Contents

    1. Introduction to the world of sparsity
    2. The wavelet transform
    3. Redundant wavelet transform
    4. Nonlinear multiscale transforms
    5. The ridgelet and curvelet transforms
    6. Sparsity and noise removal
    7. Linear inverse problems
    8. Morphological diversity
    9. Sparse blind source separation
    10. Multiscale geometric analysis on the sphere
    11. Compressed sensing.

  • Authors

    Jean-Luc Starck, Centre d'Etudes de Saclay, France
    Jean-Luc Starck is a researcher at the Institute of Research into the Fundamental Laws of the Universe (IRFU), CEA-Saclay. He holds a Ph.D. from the University of Nice-Sophia Antipolis and Observatory of Côte d'Azur and a habilitation degree from the University Paris XI. He is a former visiting researcher at the European Southern Observatory (ESO), UCLA, and the Statistics Department at Stanford University. His research interests include image processing, statistical methods in astrophysics, and cosmology. He is also author of two books, entitled Image Processing and Data Analysis: The Multiscale Approach and Astronomical Image and Data Analysis.

    Fionn Murtagh, Royal Holloway, University of London
    Fionn Murtagh directs Ireland's Science Foundation funding programs in Information and Communications Technologies and Energy. He holds a Ph.D. from the Université Paris 6 and a habilitation from Université de Strasbourg. Murtagh held professorial chairs at the University of Ulster, Queen's University Belfast, and now at Royal Holloway, University of London. He is a Fellow of the International Association for Pattern Recognition, a Fellow of the British Computer Society, and an elected Member of the Royal Irish Academy.

    Jalal M. Fadili, Ecole Nationale Supérieure d'Ingénieurs de Caen, France
    Jalal M. Fadili graduated from the Ecole Nationale Supérieure d'Ingénieurs (ENSI) de Caen, France, and received MSc and Ph.D. degrees in signal and image processing from the University of Caen. He was a Research Associate with the University of Cambridge (McDonnell–Pew Fellow) from 1999 to 2000. He has been an Associate Professor of signal and image processing since September 2001 at ENSI. He was a visitor at several universities (QUT-Australia, Stanford University, CalTech, EPFL). His research interests include mathematical signal and image processing, statistics, optimization theory, and sparse representations.

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