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008			150903s2009 xxu| o |||| 0|eng d
020			_a9780387692777 _99780387692777
024	7		_a10.1007/9780387692777 _2doi
035			_avtls000332014
039		9	_a201509030212 _bVLOAD _c201404122009 _dVLOAD _c201404091735 _dVLOAD _y201402041018 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA315-316
100	1		_aScherzer, Otmar. _eautor _9304297
245	1	0	_aVariational Methods in Imaging / _cby Otmar Scherzer, Markus Grasmair, Harald Grossauer, Markus Haltmeier, Frank Lenzen.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aApplied Mathematical Sciences, _x0066-5452 ; _v167
500			_aSpringer eBooks
505	0		_aFundamentals of Imaging -- Case Examples of Imaging -- Image and Noise Models -- Regularization -- Variational Regularization Methods for the Solution of Inverse Problems -- Convex Regularization Methods for Denoising -- Variational Calculus for Non-convex Regularization -- Semi-group Theory and Scale Spaces -- Inverse Scale Spaces -- Mathematical Foundations -- Functional Analysis -- Weakly Differentiable Functions -- Convex Analysis and Calculus of Variations.
520			_aThis book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view. Key Features: - Introduces variational methods with motivation from the deterministic, geometric, and stochastic point of view - Bridges the gap between regularization theory in image analysis and in inverse problems - Presents case examples in imaging to illustrate the use of variational methods e.g. denoising, thermoacoustics, computerized tomography - Discusses link between non-convex calculus of variations, morphological analysis, and level set methods - Analyses variational methods containing classical analysis of variational methods, modern analysis such as G-norm properties, and non-convex calculus of variations - Uses numerical examples to enhance the theory This book is geared towards graduate students and researchers in applied mathematics. It can serve as a main text for graduate courses in image processing and inverse problems or as a supplemental text for courses on regularization. Researchers and computer scientists in the area of imaging science will also find this book useful.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aGrasmair, Markus. _eautor _9304298
700	1		_aGrossauer, Harald. _eautor _9304299
700	1		_aHaltmeier, Markus. _eautor _9304300
700	1		_aLenzen, Frank. _eautor _9304301
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387309316
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-69277-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c279784 _d279784