Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations 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 avoids a succession of theorems and proofs by providing an informal description of the analysis goals and building a 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 textbook 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 who are interested in expanding their understanding and knowledge. The book is accompanied by MATLAB companion code that reproduces most of the results demonstrated in the book.
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