Compressed Sensing with Side Information on the Feasible Region

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Science & Nature, Technology, Electronics, General Computing
Cover of the book Compressed Sensing with Side Information on the Feasible Region by Mohammad Rostami, Springer International Publishing
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Author: Mohammad Rostami ISBN: 9783319003665
Publisher: Springer International Publishing Publication: May 15, 2013
Imprint: Springer Language: English
Author: Mohammad Rostami
ISBN: 9783319003665
Publisher: Springer International Publishing
Publication: May 15, 2013
Imprint: Springer
Language: English

This book discusses compressive sensing in the presence of side information. Compressive sensing is an emerging technique for efficiently acquiring and reconstructing a signal. Interesting instances of Compressive Sensing (CS) can occur when, apart from sparsity, side information is available about the source signals. The side information can be about the source structure, distribution, etc. Such cases can be viewed as extensions of the classical CS. In these cases we are interested in incorporating the side information to either improve the quality of the source reconstruction or decrease the number of samples required for accurate reconstruction. In this book we assume availability of side information about the feasible region. The main applications investigated are image deblurring for optical imaging, 3D surface reconstruction, and reconstructing spatiotemporally correlated sources. The author shows that the side information can be used to improve the quality of the reconstruction compared to the classic compressive sensing. The book will be of interest to all researchers working on compressive sensing, inverse problems, and image processing.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book discusses compressive sensing in the presence of side information. Compressive sensing is an emerging technique for efficiently acquiring and reconstructing a signal. Interesting instances of Compressive Sensing (CS) can occur when, apart from sparsity, side information is available about the source signals. The side information can be about the source structure, distribution, etc. Such cases can be viewed as extensions of the classical CS. In these cases we are interested in incorporating the side information to either improve the quality of the source reconstruction or decrease the number of samples required for accurate reconstruction. In this book we assume availability of side information about the feasible region. The main applications investigated are image deblurring for optical imaging, 3D surface reconstruction, and reconstructing spatiotemporally correlated sources. The author shows that the side information can be used to improve the quality of the reconstruction compared to the classic compressive sensing. The book will be of interest to all researchers working on compressive sensing, inverse problems, and image processing.

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