Blind Image Deconvolution Theory and Applications

The always-increasing demand for enhancing the quality of images for consumer applications, medical or biological imaging, in remote sensing, and for astronomy, to cite only a few, has fueled a resurgence of research into blind deconvolution techniques. Blind Image Deconvolution: Theory and Applications surveys the current state of research and practice as presented by the most recognized experts in the field. This comprehensive treatise discusses Bayesian methods, single- and multi-channel techniques, adaptive and multi-frame methods, and a host of applications to multimedia processing, astronomy, remote sensing imagery, and medical and biological imaging at the whole-body, small-part, and cellular levels.

Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its theoretical and practical implications. The field of blind image deconvolution has several applications in different areas such as image restoration, microscopy, medical imaging, biological imaging, remote sensing, astronomy, nondestructive testing, geophysical prospecting, and many others. Blind Image Deconvolution: Theory and Applications surveys the current state of research and practice as presented by the most recognized experts in the field, thus filling a gap in the available literature on blind image deconvolution.Explore the gamut of blind image deconvolution approaches and algorithms that currently exist and follow the current research trends into the future. This comprehensive treatise discusses Bayesian techniques, single- and multi-channel methods, adaptive and multi-frame techniques, and a host of applications to multimedia processing, astronomy, remote sensing imagery, and medical and biological imaging at the whole-body, small-part, and cellular levels. Everything you need to step into this dynamic field is at your fingertips in this unique, self-contained masterwork.For image enhancement and restoration without a priori information, turn to Blind Image Deconvolution: Theory and Applications for the knowledge and techniques you need to tackle real-world problems.

BLIND IMAGE DECONVOLUTION: PROBLEM FORMULATION AND EXISTING APPROACHES; Tom E. Bishop, S. Derin Babacan, Bruno Amizic, Aggelos K. Katsaggelos, Tony Chan, and Rafael MolinaIntroductionMathematical Problem FormulationClassification of Blind Image Deconvolution MethodologiesBayesian Framework for Blind Image DeconvolutionBayesian Modeling of Blind Image DeconvolutionBayesian Inference Methods in Blind Image DeconvolutionNon-Bayesian Blind Image Deconvolution ModelsConclusionsReferencesBLIND IMAGE DECONVOLUTION USING BUSSGANG TECHNIQUES: APPLICATIONS TO IMAGE DEBLURRING AND TEXTURE SYNTHESIS; Patrizio Campisi, Alessandro Neri, Stefania Colonnese, Gianpiero Panci, and Gaetano ScaranoIntroductionBussgang ProcessesSingle-Channel Bussgang DeconvolutionMultichannel Bussgang deconvolutionConclusionsReferencesBLIND MULTIFRAME IMAGE DECONVOLUTION USING ANISOTROPIC SPATIALLY ADAPTIVE FILTERING FOR DENOISING AND REGULARIZATION; Vladimir Katkovnik, Karen Egiazarian, and Jaakko AstolaIntroductionObservation Model and PreliminariesFrequency Domain EquationsProjection Gradient OptimizationAnisotropic LPA-ICI Spatially Adaptive FilteringBlind Deconvolution AlgorithmIdentifiability and ConvergenceSimulationsConclusionsAcknowledgmentsReferencesBAYESIAN METHODS BASED ON VARIATIONAL APPROXIMATIONS FOR BLIND IMAGE DECONVOLUTION; Aristidis Likas and Nikolas P. GalatsanosIntroductionBackground on Variational MethodsVariational Blind DeconvolutionNumerical ExperimentsConclusions and Future WorkAPPENDIX A: Computation of the Variational Bound F(q,?)APPENDIX B: Maximization of F(q,?)ReferencesDECONVOLUTION OF MEDICAL IMAGES FROM MICROSCOPIC TO WHOLE BODY IMAGES; Oleg V. Michailovich and Dan R. AdamIntroductionNonblind DeconvolutionBlind Deconvolution in Ultrasound ImagingBlind Deconvolution in SPECTBlind Deconvolution in Confocal MicroscopySummaryReferencesBAYESIAN ESTIMATION OF BLUR AND NOISE IN REMOTE SENSING IMAGING; André Jalobeanu, Josiane Zerubia, and Laure Blanc-FéraudIntroductionThe Forward ModelBayesian Estimation: Invert the Forward ModelPossible Improvements and Further DevelopmentResultsConclusionsAcknowledgmentsReferencesDECONVOLUTION AND BLIND DECONVOLUTION IN ASTRONOMY; Eric Pantin, Jean-luc Starck, and Fionn MurtaghIntroductionThe Deconvolution ProblemLinear Regularized MethodsCLEANBayesian MethodologyIterative Regularized MethodsWavelet-Based DeconvolutionDeconvolution and ResolutionMyopic and Blind DeconvolutionConclusions and Chapter SummaryAcknowledgmentsReferencesMULTIFRAME BLIND DECONVOLUTION COUPLED WITH FRAME REGISTRATION AND RESOLUTION ENHANCEMENT; Filip Šroubek, Jan Flusser, and Gabriel CristóbalIntroductionMathematical ModelPolyphase FormulationReconstruction of Volatile BlursBlind SuperresolutionExperimentsConclusionsAcknowledgmentsReferencesBLIND RECONSTRUCTION OF MULTIFRAME IMAGERY BASED ON FUSION AND CLASSIFICATION; Dimitrios Hatzinakos, Alexia Giannoula, and Jianxin HanIntroductionSystem OverviewRecursive Inverse Filtering with Finite Normal-Density Mixtures (RIF-FNM)Optimal Filter AdaptationEffects of NoiseThe Fusion and Classification Recursive Inverse Filtering Algorithm (FAC-RIF)Experimental ResultsFinal RemarksReferencesBLIND DECONVOLUTION AND STRUCTURED MATRIX COMPUTATIONS WITH APPLICATIONS TO ARRAY IMAGING; Michael K. Ng and Robert J. PlemmonsIntroductionOne-Dimensional Deconvolution FormulationRegularized and Constrained TLS FormulationNumerical AlgorithmsTwo-Dimensional Deconvolution ProblemsNumerical ExamplesApplication: High-Resolution Image ReconstructionConcluding Remarks and Current WorkAcknowledgmentsReferencesINDEX