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

书名：图像处理与分析

作者：（美国）(TonyF.Chan)陈繁昌

ISBN：9787030234858

类别：计算机科学

定价：88.00 元

出版社：科学出版社

出版时间：2009

开本：1/16

内容简介

目录

内容简介

Image Processing and Analysis:

Variational,PDE,Wavelet,and Stochastic Methodsis systematic and well organized,The authors first investigate the geometric,functional,and atomic structures of images and then rigorously develop and analyzes ever alimage processors.

The book is comprehensive and integrative, covering the four most powerful classes of mathematical tools in contemporary image analysis and processing while exploring the irintrinsic connection sand integration.

The material is balanced in theory and computation, following a solid theoretic alanalysis of model building and performance with computational implementation and numerical examples.

This book is written for graduate students and researcher sinapplied mathematics, computerscience, electrical engineering, 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 the latest advances.

目录

ListofFigures

Preface

1Introduction

1.1DawningoftheEraofImagingSciences

1.1.1ImageAcquisition

1.1.2ImageProcessing

1.1.3ImageInterpretationandVisualIntelligence

1.2ImageProcessingbyExamples

1.2.1ImageContrastEnhancement

1.2.2ImageDenoisirg

1.2.3ImageDeblurring

1.2.4ImageInpainting

1.2.5ImageSegmentation

1.3AnOverviewofMethodologiesinImageProcessing

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

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