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008			230828s2023 si | o |||| 0|eng d
020			_a9789819937509
024	7		_a10.1007/978-981-99-3750-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637 _b2023 EB
100	1		_aHe, Chuan _eautor _9689615
245	1	0	_aParallel Operator Splitting Algorithms with Application to Imaging Inverse Problems _cby Chuan He, Changhua Hu
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvanced and Intelligent Manufacturing in China _x2731-5991
505	0		_aIntroduction -- Mathematical Fundamentals -- Ill-poseness of imaging inverse problems and regularization for detail preservation -- Fast parameter estimation in TV-based image restoration -- Parallel alternating derection method of multipliers with application to image restoration -- Parallel primal-dual method with application to image restoration.
520			_aImage denoising, image deblurring, image inpainting, super-resolution, and compressed sensing reconstruction have important application value in engineering practice, and they are also the hot frontiers in the field of image processing. This book focuses on the numerical analysis of ill condition of imaging inverse problems and the methods of solving imaging inverse problems based on operator splitting. Both algorithmic theory and numerical experiments have been addressed. The book is divided into six chapters, including preparatory knowledge, ill-condition numerical analysis and regularization method of imaging inverse problems, adaptive regularization parameter estimation, and parallel solution methods of imaging inverse problem based on operator splitting. Although the research methods in this book take image denoising, deblurring, inpainting, and compressed sensing reconstruction as examples, they can also be extended to image processing problems such as image segmentation, hyperspectral decomposition, and image compression. This book can benefit teachers and graduate students in colleges and universities, or be used as a reference for self-study or further study of image processing technology engineers. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.
988			_aSpringer_Computer_2023
650		7	_2embne _9413188 _aProceso digital de imágenes
700	1		_9689617 _aHu, Changhua _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-3750-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI