000			04276nam a22004815i 4500
001			978-1-4419-7011-4
003			DE-He213
005			20140220084511.0
007			cr nn 008mamaa
008			100812s2010 xxu| s |||| 0|eng d
020			_a9781441970114 _9978-1-4419-7011-4
024	7		_a10.1007/978-1-4419-7011-4 _2doi
050		4	_aQA401-425
072		7	_aPBKJ _2bicssc
072		7	_aMAT034000 _2bisacsh
082	0	4	_a511.4 _223
100	1		_aElad, Michael. _eauthor.
245	1	0	_aSparse and Redundant Representations _h[electronic resource] : _bFrom Theory to Applications in Signal and Image Processing / _cby Michael Elad.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aXX, 376p. 161 illus., 41 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aSparse and Redundant Representations – Theoretical and Numerical Foundations -- Prologue -- Uniqueness and Uncertainty -- Pursuit Algorithms – Practice -- Pursuit Algorithms – Guarantees -- From Exact to Approximate Solutions -- Iterative-Shrinkage Algorithms -- Towards Average PerformanceAnalysis -- The Dantzig-Selector Algorithm -- From Theory to Practice – Signal and Image Processing Applications -- Sparsity-Seeking Methods in Signal Processing -- Image Deblurring – A Case Study -- MAP versus MMSE Estimation -- The Quest for a Dictionary -- Image Compression – Facial Images -- Image Denoising -- Other Applications -- Epilogue.
520			_aThe field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarantee these algorithms’ performance. With these contributions in place, major barriers in making this model practical and applicable were removed, and sparsity and redundancy became central, leading to state-of-the-art results in various disciplines. One of the main beneficiaries of this progress is the field of image processing, where this model has been shown to lead to unprecedented performance in various applications. This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing. It offers a systematic and ordered exposure to the theoretical foundations of this data model, the numerical aspects of the involved algorithms, and the signal and image processing applications that benefit from these advancements. The book is well-written, presenting clearly the flow of the ideas that brought this field of research to its current achievements. It avoids a succession of theorems and proofs by providing an informal description of the analysis goals and building this way the path to the proofs. The applications described help the reader to better understand advanced and up-to-date concepts in signal and image processing. Written as a text-book for a graduate course for engineering students, this book can also be used as an easy entry point for readers interested in stepping into this field, and for others already active in this area that are interested in expanding their understanding and knowledge. The book is accompanied by a Matlab software package that reproduces most of the results demonstrated in the book. A link to the free software is available on springer.com.
650		0	_aMathematics.
650		0	_aComputer vision.
650		0	_aMathematical optimization.
650	1	4	_aMathematics.
650	2	4	_aApproximations and Expansions.
650	2	4	_aImage Processing and Computer Vision.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aOptimization.
650	2	4	_aApplications of Mathematics.
650	2	4	_aMathematical Modeling and Industrial Mathematics.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441970107
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-7011-4
912			_aZDB-2-SMA
999			_c110715 _d110715