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＜電子ブック＞
Sparse and Redundant Representations : From Theory to Applications in Signal and Image Processing / by Michael Elad

版	1st ed. 2010.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2010
本文言語	英語
大きさ	XX, 376 p : online resource
著者標目	*Elad, Michael author SpringerLink (Online service)
件　名	LCSH:Mathematical analysis LCSH:Mathematical models LCSH:Mathematical optimization LCSH:Approximation theory LCSH:Computer vision LCSH:Signal processing FREE:Analysis FREE:Mathematical Modeling and Industrial Mathematics FREE:Optimization FREE:Approximations and Expansions FREE:Computer Vision FREE:Signal, Speech and Image Processing
一般注記	Sparse 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 The 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 HTTP:URL=https://doi.org/10.1007/978-1-4419-7011-4

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