Compressed Sensing with Side Information on the Feasible Region (Paperback, 2013 ed.)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Electrical and Computer Engineering
Release date:	May 2013
First published:	2013
Authors:	Mohammad Rostami
Dimensions:	235 x 155 x 5mm (L x W x T)
Format:	Paperback
Pages:	69
Edition:	2013 ed.
ISBN-13:	978-3-319-00365-8
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	3-319-00365-8
Barcode:	9783319003658

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