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Titlebook: Compressed Sensing with Side Information on the Feasible Region; Mohammad Rostami Book 2013 The Author(s) 2013 Compressive Sensing.Derivat

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发表于 2025-3-21 16:32:46 | 显示全部楼层 |阅读模式
书目名称Compressed Sensing with Side Information on the Feasible Region
编辑Mohammad Rostami
视频video
丛书名称SpringerBriefs in Electrical and Computer Engineering
图书封面Titlebook: Compressed Sensing with Side Information on the Feasible Region;  Mohammad Rostami Book 2013 The Author(s) 2013 Compressive Sensing.Derivat
描述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.
出版日期Book 2013
关键词Compressive Sensing; Derivative Compressive Sampling; Image Deblurring; Inverse Problems; Shack-Hartmann
版次1
doihttps://doi.org/10.1007/978-3-319-00366-5
isbn_softcover978-3-319-00365-8
isbn_ebook978-3-319-00366-5Series ISSN 2191-8112 Series E-ISSN 2191-8120
issn_series 2191-8112
copyrightThe Author(s) 2013
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发表于 2025-3-21 23:38:38 | 显示全部楼层
Book 2013d 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
发表于 2025-3-22 03:07:02 | 显示全部楼层
https://doi.org/10.1007/978-3-658-18656-2g density does not allow for recovery of field details. In this chapter, the above limitation is resolved by means of using the proposed algorithm. We propose to exploit the intrinsic property of diffusive fields as side information to improve the reconstruction results of classic CS.
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Application: Surface Reconstruction in Gradient Field,ces, situations are possible in which the available sampling density might not be sufficiently high to allow for recovery of essential surface details. In this section the above problem is resolved by means of derivative compressed sensing (DCS). The results of this study are supported by a series of numerical experiments.
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