图像处理与分析

书 名: 图像处理与分析 作 者:(美国)(TonyF.Chan)陈繁昌 出版社: 科学出版社 出版时间: 2009 ISBN: 9787030234858

• 中文名 图像处理与分析
• 定价 88.00 元
• 出版社 科学出版社
• 作者 (美国)(TonyF.Chan)陈繁昌
• 出版时间 2009

内容简介

　　Image Processin乡缺府孩凯型黑吸g and Analysis:

　　Variational,PDE,Wavelet,and St来自ochastic Methodsis systematic and well organized,The authors first investigate the geometric,function料换语练顾al,and atomic structures of images and then rigorously develop and analyzes ever alimag360百科e processors.

　　The book is comprehensive and inte难内关见成写器种抗grative, covering 过和你the four most powerful classes of mathematical tools in contemporary image analysis and processing w振令之议几hile exploring the irintrinsic connection sand integration.

　　The material is balanced in theory and computation, fol换草及音抓督胞息lowing a solid theoretic alanalysis of model building and performance with computational im院议室朝帮plementation and numerical examples.

　　This book is w革ritten for graduate students and researcher sinapplied mathematics, computerscience品见战, electrical engi训守社父盐钟调措neering, and other disciplines who are interested in problems in imaging and computervision.

　　It can beused as a reference by scientists with specific tasks in image processing, as well as by researcher swith a general interest in finding out about 不或沙介担践孔the latest advanc入货会es.

目录

　　Li引优宽语跑stofFigure口着院s

　　Preface

　　1Introduction

　　1.1DawningoftheEraofImagingSciences

　　1.1.1ImageAcquisition

　　1.1.2ImageProcessing

　　1.1.3ImageInterpretationandVisualInt思着卫elligence

　　1.2ImageP但也末盟低引层首rocessingbyExamples

　　1.2.1ImageContrastEnhancement

　　1.2.2ImageDenoisirg

　　1.2.3ImageDeblurring

　　1.2.4ImageInpainting

　向采论古措改宪　1.2.5ImageSegmentation

　　1.3AnOverviewofMethodo曾女跑手备品款代适具侵logiesinImageProcessing

　　1.3.1MorphologicalApproach

　　1.3.2FourierandSpectralAnalysis

　　1.3.3WaveletandSpace-ScaleAnalysis

　　1.3.4StochasticModeling

　　1.3.5VariaticnalMethods

　　1.3.6PartialDifferentialEquations(PDEs)

　　1.3.7DifferentApproachesAreIntrinsicallyInterconnected

　　1.4OrganizationoftheBook

　　1.5HowtoReadtheBcok

　　2SomeModernImageAnalysisTools

　　2.1GeometryofCurvesandSurfaces

　　2.1.IGeometryofCurves

　　2.1.2GeometryofSurfacesinThreeDimensions

　　2.1.3HausdorffMeasuresandDimensions

　　2.2FunctionswithBoundedVariations

　　2.2.1TotalVariatienasaRadonMeasure

　　2.2.2BasicPropertiesofBVFunctions

　　2.2.3TheCo-AreaFormula

　　2.3ElementsofThermodynamicsandStatisticalMechanics

　　2.3.1EssentialsofThermodynamics

　　2.3.2EntropyandPotentials

　　2.3.3StatisticalMechanicsofEnsembles

　　2.4BayesianStatisticalInference

　　2.4.1ImageProcessingorVisualPerceptionasInference

　　2.4.2BayesianInference:BiasDuetoPriorKnowledge

　　2.4.3BayesianMethodinImageProcessing

　　2.5LinearandNonlinearFilteringandDiffusion

　　2.5.1PointSpreadingandMarkovTransition

　　2.5.2LinearFilteringandDiffusion

　　2.5.3NonlinearFilteringandDiffusion

　　2.6WaveletsandMultiresolutionAnalysis

　　2.6.1QuestforNewImageAnalysisTools

　　2.6.2EarlyEdgeTheoryandMarr'sWavelets

　　2.6.3WindowedFrequencyAnalysisandGaborWavelets

　　2.6.4Frequency-WindowCoupling:Malvar-WilsonWavelets

　　2.6.5TheFrameworkofMultiresolutionAnalysis(MRA)

　　2.6.6FastImageAnalysisandSynthesisviaFilterBanks

　　3ImageModelingandRepresentation

　　3.1ModelingandRepresentation:What,Why,andHow

　　3.2DeterministicImageModels

　　3.2.1ImagesasDistributions(GeneralizedFunctions)

　　3.2.2LpImages

　　3.2.3SobolevImagesHn(Ω)

　　3.2.4BVImages

　　3.3WaveletsandMultiscaleRepresentation

　　3.3.1Constructionof2-DWavelets

　　3.3.2WaveletResponsestoTypicalImageFeatures

　　3.3.3BesovImagesandSparseWaveletRepresentation

　　3.4LatticeandRandomFieldRepresentation

　　3.4.1NaturalImagesofMotherNature

　　3.4.2ImagesasEnsemblesandDistributions

　　3.4.3ImagesasGibbs'Ensembles

　　3.4.4ImagesasMarkovRandomFields

　　3.4.5VisualFiltersandFilterBanks

　　3.4.6Entropy-BasedLearningofImagePatterns

　　3.5Level-SetRepresentation

　　3.5.1ClassicalLevelSets

　　3.5.2CumulativeLevelSets

　　3.5.3Level-SetSynthesis

　　3.5.4AnExample:LevelSetsofPiecewiseConstantImages

　　3.5.5HighOrderRegularityofLevelSets

　　3.5.6StatisticsofLevelSetsofNaturalImages

　　3.6TheMumford-ShahFreeBoundaryImageModel

　　3.6.1PiecewiseConstant1-DImages:AnalysisandSynthesis

　　3.6.2PiecewiseSmooth1-DImages:FirstOrderRepresentation

　　3.6.3PiecewiseSmoothI-DImages:PoissonRepresentation

　　3.6.4PiecewiseSmooth2-DImages

　　3.6.5TheMumford-ShahModel

　　3.6.6TheRoleofSpecialBVImages

　　4ImageDenoising

　　4.1Noise:Origins.Physics.andModels

　　4.l.1OriginsandPhysicsofNoise

　　4.1.2ABriefOverviewof1-DStochasticSignals

　　4.1.3StochasticModelsofNoises

　　4.1.4AnalogWhiteNoisesasRandomGeneralizedFunctions

　　4.1.5RandomSignalsfromStochasticDifferentialEquations

　　4.l.62-DStochasticSpatialSignals:RandomFields

　　4.2LinearDenoising:LowpassFiltering

　　4.2.1Signalvs.Noise

　　4.2.2DenoisingviaLinearFiltersandDiffusion

　　4.3Data-DrivenOptimalFiltering:WienerFilters

　　4.4WaveletShrinkageDenoising

　　4.4.1Shrinkage:Quasi-statisticalEstimationofSingletons

　　4.4.2Shrinkage:VariationalEstimationofSingletons

　　4.4.3DenoisingviaShrinkingNoisyWaveletComponents

　　4.4.4VariationalDenoisingofNoisyBesovImages

　　4.5VariationalDenoisingBasedonBVImageModel

　　4.5.1TV.RobustStatistics.andMedian

　　4.5.2TheRoleofTVandBVImageModel

　　4.5.3BiasedIteratedMedianFiltering

　　4.5.4Rudin.Osher.andFatemi'sTVDenoisingModel

　　4.5.5ComputationalApproachestoTVDenoising

　　4.5.6DualityfortheTVDenoisingModel

　　4.5.7SolutionStructuresoftheTVDenoisingModel

　　4.6DenoisingviaNonlinearDiffusionandScale-SpaceTheory

　　4.6.1PeronaandMalik'sNonlinearDiffusionModel

　　4.6.2AxiomaticScale-SpaceTheory

　　4.7DenoisingSalt-and-PepperNoise

　　4.8MultichannelTVDenoising

　　4.8.1VariationalTVDenoisingofMultichannelImages

　　4.8.2ThreeVersionsofTV[u]

　　5ImageDeblurring

　　5.1Blur:PhysicalOriginsandMathematicalModels

　　5.1.1PhysicalOrigins

　　5.1.2MathematicalModelsofBlurs

　　5.1.3Linearvs.NonlinearBlurs

　　5.2Ill-posednessandRegularization

　　5.3DeblurringwithWienerFilters

　　5.3.1IntuitiononFilter-BasedDeblurring

　　5.3.2WienerFiltering

　　5.4DeblurringofBVImageswithKnownPSF

　　5.4.1TheVariationalModel

　　5.4.2ExistenceandUniqueness

　　5.4.3Computation

　　5.5VariationalBlindDeblurringwithUnknownPSF

　　5.5.1ParametricBlindDeblurring

　　5.5.2Parametric-Field-BasedBlindDeblurring

　　5.5.3NonparametricBlindDeblurring

　　6ImageInpainting

　　6.1ABriefReviewonClassicalInterpolationSchemes

　　6.1.1PolynomialInterpolation

　　6.1.2TrigonometricPolynomialInterpolation

　　6.1.3SplineInterpolation

　　6.1.4Shannon'sSamplingTheorem

　　6.1.5RadialBasisFunctionsandThin-PlateSplines

　　6.2ChallengesandGuidelinesfor2-DImageInpainting

　　6.2.1MainChallengesforImageInpainting

　　6.2.2GeneralGuidelinesforImageInpainting

　　6.3InpaintingofSobolevImages:Green'sFormulae

　　6.4GeometricModelingofCurvesandImages

　　6.4.1GeometricCurveModels

　　6.4.22-.3-PointAccumulativeEnergies.Length.andCurvature.

　　6.4.3ImageModelsviaFunctionalizingCurveModels

　　6.4.4ImageModelswithEmbeddedEdgeModels

　　6.5InpaintingBVImages(viatheTVRadonMeasure)

　　6.5.1FormulationoftheTVInpaintingModel

　　6.5.2JustificationofTVInpaintingbyVisualPerception

　　6.5.3ComputationofTVlnpainting

　　6.5.4DigitalZoomingBasedonTVInpainting

　　6.5.5Edge-BasedImageCodingviaInpainting

　　6.5.6MoreExamplesandApplicationsofTVInpainting

　　6.6ErrorAnalysisforImageInpainting

　　6.7InpaintingPiecewiseSmoothImagesviaMumfordandShah

　　6.8ImageInpaintingviaEuler'sElasticasandCurvatures

　　6.8.1InpaintingBasedontheElasticaImageModel

　　6.8.2InpaintingviaMumford-Shah-EulerImageModel

　　6.9InpaintingofMeyer'sTexture

　　6.10ImageInpaintingwithMissingWaveletCoefficients

　　6.11PDEInpainting:Transport.Diffusion.andNavier-Stokes

　　6.11.1SecondOrderInterpolationModels

　　6.11.2AThirdOrderPDEInpaintingModelandNavier-Stokes

　　……

　　7ImageSegmentation

　　Bibliography

　　Index

　　……