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Sparse image and signal processing[electronic resource] :wavelets and related geometric multiscale analysis /

纪录类型:	书目-电子资源 : Monograph/item
[NT 15000414] null:	621.367
[NT 47271] Title/Author:	Sparse image and signal processing : wavelets and related geometric multiscale analysis // Jean-Luc Starck, Fionn Murtagh, Jalal Fadili.
[NT 51403] remainder title:	Sparse Image & Signal Processing
作者:	Starck, Jean-Luc.
[NT 51406] other author:	Murtagh, Fionn.
出版者:	Cambridge : : Cambridge University Press,, 2015.
面页册数:	xix, 428 p. : : digital ;; 24 cm.
附注:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
标题:	Transformations (Mathematics)
标题:	Signal processing.
标题:	Image processing.
标题:	Sparse matrices.
标题:	Wavelets (Mathematics)
ISBN:	9781316104514
ISBN:	9781107088061
[NT 15000228] null:	Introduction to the world of sparsity -- The wavelet transform -- Redundant wavelet transform -- Nonlinear multiscale transforms -- Multiscale geometric transforms -- Sparsity and noise removal -- Linear inverse problems -- Morphological diversity -- Sparse blind source separation -- Dictionary learning -- Three-dimensional sparse representations -- Multiscale geometric analysis on the sphere -- Compressed sensing -- This book's take home message.
[NT 15000229] null:	This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
电子资源:	https://doi.org/10.1017/CBO9781316104514

Sparse image and signal processing[electronic resource] :wavelets and related geometric multiscale analysis /
Starck, Jean-Luc.

Sparse image and signal processingwavelets and related geometric multiscale analysis /[electronic resource] :Sparse Image & Signal ProcessingJean-Luc Starck, Fionn Murtagh, Jalal Fadili. - Second edition. - Cambridge :Cambridge University Press,2015. - xix, 428 p. :digital ;24 cm.

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Introduction to the world of sparsity -- The wavelet transform -- Redundant wavelet transform -- Nonlinear multiscale transforms -- Multiscale geometric transforms -- Sparsity and noise removal -- Linear inverse problems -- Morphological diversity -- Sparse blind source separation -- Dictionary learning -- Three-dimensional sparse representations -- Multiscale geometric analysis on the sphere -- Compressed sensing -- This book's take home message.

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.

ISBN: 9781316104514Subjects--Topical Terms:

228448
Transformations (Mathematics)

LC Class. No.: QA601 / .S785 2015

Dewey Class. No.: 621.367

Sparse image and signal processing[electronic resource] :wavelets and related geometric multiscale analysis /
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020 $a 9781107088061 $q (paper)
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050 4 $a QA601 $b .S785 2015
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100 1 $a Starck, Jean-Luc. $3 712109
245 1 0 $a Sparse image and signal processing $h [electronic resource] : $b wavelets and related geometric multiscale analysis / $c Jean-Luc Starck, Fionn Murtagh, Jalal Fadili.
246 3 $a Sparse Image & Signal Processing
250 $a Second edition.
260 $a Cambridge : $b Cambridge University Press, $c 2015.
300 $a xix, 428 p. : $b digital ; $c 24 cm.
500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
505 0 $a Introduction to the world of sparsity -- The wavelet transform -- Redundant wavelet transform -- Nonlinear multiscale transforms -- Multiscale geometric transforms -- Sparsity and noise removal -- Linear inverse problems -- Morphological diversity -- Sparse blind source separation -- Dictionary learning -- Three-dimensional sparse representations -- Multiscale geometric analysis on the sphere -- Compressed sensing -- This book's take home message.
520 $a This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
650 0 $a Transformations (Mathematics) $3 228448
650 0 $a Signal processing. $3 174043
650 0 $a Image processing. $3 342096
650 0 $a Sparse matrices. $3 470188
650 0 $a Wavelets (Mathematics) $3 393849
700 1 $a Murtagh, Fionn. $3 712110
700 1 $a Fadili, Jalal. $3 712111
856 4 0 $u https://doi.org/10.1017/CBO9781316104514

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