Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity
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. Get and download textbook Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity for free
Categories: Image processing, Signal processing. Contributors: Jean-Luc Starck - Author. Format: Hardcover
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 Sparse Image and Signal Processing new edition
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Categories: Image processing, Signal processing, Wavelets (Mathematics). Contributors: Jean-Luc Starck - Author. Format: Hardcover
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Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, ISBN-13: 9780521119139, ISBN-10: 0521119138
author fionn murtagh author jalal m fadili author jean luc starck format hardback language english publication year 10 05 2010 subject computing it subject 2 computing professional programming payment shipping rates returns sparse image and signal processing wavelets curvelets morphological diversity title sparse image and signal processing wavelets curvelets morphological diversity author fadili jalal m murtagh fionn starck jean luc binding hardcover isbn 0 0521119138 13 9780521119139 publisher
Sparse Image and Signal Processing Textbook
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
A final chapter explores a paradigm shift in signal processing, showing that previous limits to information
