001476520 000__ 07063cam\\22006857a\4500
001476520 001__ 1476520
001476520 003__ OCoLC
001476520 005__ 20231003174425.0
001476520 006__ m\\\\\o\\d\\\\\\\\
001476520 007__ cr\un\nnnunnun
001476520 008__ 230902s2023\\\\si\\\\\\ob\\\\001\0\eng\d
001476520 019__ $$a1395947497$$a1396894014
001476520 020__ $$a9789819937509$$q(electronic bk.)
001476520 020__ $$a9819937507$$q(electronic bk.)
001476520 020__ $$z9819937493
001476520 020__ $$z9789819937493
001476520 0247_ $$a10.1007/978-981-99-3750-9$$2doi
001476520 035__ $$aSP(OCoLC)1396064746
001476520 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dQGK
001476520 049__ $$aISEA
001476520 050_4 $$aTA1637
001476520 08204 $$a621.367$$223/eng/20230913
001476520 1001_ $$aHe, Chuan.
001476520 24510 $$aParallel operator splitting algorithms with application to imaging inverse problems /$$cChuan He, Changhua Hu.
001476520 260__ $$aSingapore :$$bSpringer,$$c2023.
001476520 300__ $$a1 online resource (208 p.).
001476520 336__ $$atext$$btxt$$2rdacontent
001476520 337__ $$acomputer$$bc$$2rdamedia
001476520 338__ $$aonline resource$$bcr$$2rdacarrier
001476520 4901_ $$aAdvanced and Intelligent Manufacturing in China
001476520 500__ $$a5.2.1 A General Description of the Regularized Image Restoration Objective Function
001476520 504__ $$aIncludes bibliographical references and index.
001476520 5050_ $$aIntro -- Preface -- Contents -- About the Authors -- 1 Introduction -- 1.1 Implications for Image Restoration -- 1.2 Regularization Methods for Image Restoration -- 1.2.1 Image Degradation Mechanisms and Degradation Modeling -- 1.2.2 Regularization Methods Based on Variational Partial Differential Equations -- 1.2.3 Regularization Methods Based on Wavelet Frame Theory -- 1.2.4 Regularization Methods Based on Sparse Representation of Images -- 1.2.5 Random Field-Based Regularization Methods -- 1.3 Nonlinear Iterative Algorithm for Image Restoration -- 1.3.1 Traditional Methods
001476520 5058_ $$a1.3.2 Operator Splitting Methods -- 1.3.3 Convergence Analysis of the Splitting Algorithms -- 1.3.4 Adaptive Estimation of the Regularization Parameter -- References -- 2 Mathematical Fundamentals -- 2.1 Summarize -- 2.2 Convolution -- 2.2.1 One-Dimensional Discrete Convolution -- 2.2.2 Two-Dimensional Discrete Convolution -- 2.3 Fourier Transform and Discrete Fourier Transform -- 2.4 Theory and Methods of Fixed-Points in Hilbert Spaces -- 2.4.1 Hilbert Space -- 2.4.2 Non-expansive Operators with Fixed-Point Iterations -- 2.4.3 Maximally Monotone Operator
001476520 5058_ $$a2.4.4 Solution of the l1-ball Projection Problem -- Reference -- 3 Ill-Poseness of Imaging Inverse Problems and Regularization for Detail Preservation -- 3.1 Summarize -- 3.2 Typical Types of Image Blur -- 3.3 The Ill-Posed Nature of Image Deblurring -- 3.3.1 Discretization of Convolution Equations and Ill-Posed Analysis of Blur Matrices -- 3.3.2 Image Restoration Based on Inverse Filter -- 3.4 Tikhonov Image Regularization -- 3.4.1 Tikhonov Regularization Idea -- 3.4.2 Wiener Filtering -- 3.4.3 Constrained Least Square Filtering -- 3.5 Detail-Preserving Regularization for Image
001476520 5058_ $$a3.5.1 Total Generalized Variational Regularization Model -- 3.5.2 Shearlet Regularization Model -- 3.6 Image Quality Evaluation -- References -- 4 Fast Parameter Estimation in TV-Based Image Restoration -- 4.1 Summarize -- 4.2 Overview of Adaptive Parameter Estimation Methods in TV Image Restoration -- 4.3 Fast Adaptive Parameter Estimation Based on ADMM and Discrepancy Principle -- 4.3.1 Augmented Lagrangian Model for TV Regularized Problem -- 4.3.2 Algorithm Derivation -- 4.3.3 Convergence Analysis -- 4.3.4 Parameter Settings -- 4.4 Extension of Fast Adaptive Parameter Estimation Algorithm
001476520 5058_ $$a4.4.1 Equivalent Splitting Bregman Algorithm -- 4.4.2 Interval Constrained TV Image Restoration with Fast Adaptive Parameter Estimation -- 4.5 Experimental Results -- 4.5.1 Experiment 1: Implications for Significance Regularization Parameter Estimation -- 4.5.2 Experiment 2-Comparison with Other Adaptive Algorithms -- 4.5.3 Experiment 3-Comparison of Denoising Experiments -- References -- 5 Parallel Alternating Derection Method of Multipliers with Application to Image Restoration -- 5.1 Summarize -- 5.2 Parallel Alternating Direction Method of Multipliers
001476520 506__ $$aAccess limited to authorized users.
001476520 520__ $$aImage denoising, image deblurring, image inpainting, super-resolution, and compressed sensing reconstruction have important application value in engineering practice, and they are also the hot frontiers in the field of image processing. This book focuses on the numerical analysis of ill condition of imaging inverse problems and the methods of solving imaging inverse problems based on operator splitting. Both algorithmic theory and numerical experiments have been addressed. The book is divided into six chapters, including preparatory knowledge, ill-condition numerical analysis and regularization method of imaging inverse problems, adaptive regularization parameter estimation, and parallel solution methods of imaging inverse problem based on operator splitting. Although the research methods in this book take image denoising, deblurring, inpainting, and compressed sensing reconstruction as examples, they can also be extended to image processing problems such as image segmentation, hyperspectral decomposition, and image compression. This book can benefit teachers and graduate students in colleges and universities, or be used as a reference for self-study or further study of image processing technology engineers. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.
001476520 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 13, 2023).
001476520 650_0 $$aImage processing$$xData processing.
001476520 650_0 $$aAlgorithms.
001476520 655_0 $$aElectronic books.
001476520 7001_ $$aHu, Changhua.
001476520 77608 $$iPrint version:$$aHe, Chuan$$tParallel Operator Splitting Algorithms with Application to Imaging Inverse Problems$$dSingapore : Springer,c2023$$z9789819937493
001476520 830_0 $$aAdvanced and Intelligent Manufacturing in China.
001476520 852__ $$bebk
001476520 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3750-9$$zOnline Access$$91397441.1
001476520 909CO $$ooai:library.usi.edu:1476520$$pGLOBAL_SET
001476520 980__ $$aBIB
001476520 980__ $$aEBOOK
001476520 982__ $$aEbook
001476520 983__ $$aOnline
001476520 994__ $$a92$$bISE