Table of Contents

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.

About the Author

Michael Elad has been working at The Technion in Haifa, Israel, since 2003 and is currently an Associate Professor. He is one of the leaders in the field of sparse representations. He does prolific research in mathematical signal processing with more than 60 publications in top ranked journals. He is very well recognized and respected in the scientific community.

Reviews

From the reviews: "This book approaches sparse and redundant representations from an engineering perspective and emphasizes their use as a signal modeling tool and their application in image and signal processing. ! This book is well suited to practitioners in the signals and image processing community ! . The public availability of the source code used in the numerical experiments throughout the book could help students make the transition from theory to practice and allow them to get hands-on experience with the inner workings of the various algorithms." (Ewout van den Berg, SIAM Review, Vol. 53 (4), 2011)