000			03303nam a22005415i 4500
001			978-3-319-00366-5
003			DE-He213
005			20200421112234.0
007			cr nn 008mamaa
008			130516s2013 gw | s |||| 0|eng d
020			_a9783319003665 _9978-3-319-00366-5
024	7		_a10.1007/978-3-319-00366-5 _2doi
050		4	_aT385
050		4	_aTA1637-1638
050		4	_aTK7882.P3
072		7	_aUYQV _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.6 _223
100	1		_aRostami, Mohammad. _eauthor.
245	1	0	_aCompressed Sensing with Side Information on the Feasible Region _h[electronic resource] / _cby Mohammad Rostami.
264		1	_aHeidelberg : _bSpringer International Publishing : _bImprint: Springer, _c2013.
300			_aXIII, 69 p. 20 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
505	0		_aIntroduction -- Compressed Sensing -- Compressed Sensing with Side Information on Feasible Region -- Application: Image Deblurring for Optical Imaging -- Application: Surface Reconstruction in Gradient Field -- Conclusions and Future Work.
520			_aThis 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.
650		0	_aComputer science.
650		0	_aComputer graphics.
650		0	_aComputer mathematics.
650		0	_aStatistical physics.
650		0	_aDynamical systems.
650	1	4	_aComputer Science.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aStatistical Physics, Dynamical Systems and Complexity.
650	2	4	_aComputational Science and Engineering.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319003658
830		0	_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-00366-5
912			_aZDB-2-ENG
942			_cEBK
999			_c58173 _d58173