Sparse image and signal processing: Wavelets, curvelets, morphological diversity

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Read more Reviews Editorial reviews. Publisher Synopsis 'This book is well organized, and it covers the theory and application of multiscale imaging and image processing. User-contributed reviews Add a review and share your thoughts with other readers. Be the first. Add a review and share your thoughts with other readers. Image processing. Sparse matrices. Wavelets Mathematics Traitement du signal.

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Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity

Wim van Drongelen. Olek C Zienkiewicz. So, in this paper we focus on the nonnegative dictionary learning for signal representation. The authors give some guidance into understanding how sparsity helps in signal and image processing, what some benefits of overcomplete representations are, when to use isotropic wavelets for image processing, why morphological diversity can be helpful, and how to choose between analysis and synthesis priors for regularization in inverse problems. Not in United States?

Similar Items. Introduction to the world of sparsity -- The wavelet transform -- Redundant wavelet transform -- Nonlinear multiscale transforms -- The ridgelet and curvelet transforms -- Sparsity and noise removal -- Linear inverse problems -- Morphological diversity -- Sparse blind source separation -- Multiscale geometric analysis on the sphere -- Compressed sensing.

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Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity: Computer Science Books @ trapencerde.tk Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; ) [Book Reviews]. Abstract.

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Popular Features. New Releases. Description 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 are available for download at the associated web site.

Table of contents 1.

Introduction to the world of sparsity; 2. The wavelet transform; 3. Redundant wavelet transform; 4.

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Nonlinear multiscale transforms; 5. The ridgelet and curvelet transforms; 6.

Abstract or Description

Sparsity and noise removal; 7. Linear inverse problems; 8. Morphological diversity; 9. Sparse blind source separation; Multiscale geometric analysis on the sphere;