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Sparse image and signal processingwa...

Fadili, Jalal.

Sparse image and signal processingwavelets and related geometric multiscale analysis /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sparse image and signal processingJean-Luc Starck, Fionn Murtagh, Jalal Fadili.
Reminder of title:	wavelets and related geometric multiscale analysis /
remainder title:	Sparse Image & Signal Processing
Author:	Starck, Jean-Luc.
other author:	Murtagh, Fionn.
Published:	Cambridge :Cambridge University Press,2015.
Description:	xix, 428 p. :digital ;24 cm.
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Transformations (Mathematics)
Online resource:	https://doi.org/10.1017/CBO9781316104514
ISBN:	9781316104514$q(electronic bk.)

Sparse image and signal processingwavelets 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: 9781316104514$q(electronic bk.)Subjects--Topical Terms:

182635
Transformations (Mathematics)

LC Class. No.: QA601 / .S785 2015

Dewey Class. No.: 621.367

Sparse image and signal processingwavelets and related geometric multiscale analysis /
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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 182635
650 0 $a Signal processing. $3 182382
650 0 $a Image processing. $3 182627
650 0 $a Sparse matrices. $3 211248
650 0 $a Wavelets (Mathematics) $3 182734
700 1 $a Murtagh, Fionn. $3 871088
700 1 $a Fadili, Jalal. $3 871089
856 4 0 $u https://doi.org/10.1017/CBO9781316104514