Title: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Abstract: This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging. Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing.
Publication Year: 2011
Publication Date: 2011-07-21
Language: en
Type: book
Access and Citation
Cited By Count: 2020
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot