DIGITAL IMAGE PROCESSING THIRD EDITION2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

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Preface15

Acknowledgments19

The Book Web Site20

About the Authors21

1 Introduction23

1.1 What Is Digital Image Processing？23

1.2 The Origins of Digital Image Processing25

1.3 Examples of Fields that Use Digital Image Processing29

1.3.1 Gamma-Ray Imaging30

1.3.2 X-Ray Imaging31

1.3.3 Imaging in the Ultraviolet Band33

1.3.4 Imaging in the Visible and Infrared Bands34

1.3.5 Imaging in the Microwave Band40

1.3.6 Imaging in the Radio Band42

1.3.7 Examples in which Other Imaging Modalities Are Used42

1.4 Fundamental Steps in Digital Image Processing47

1.5 Components of an Image Processing System50

Summary53

References and Further Reading53

2 Digital Image Fundamentals57

2.1 Elements of Visual Perception58

2.1.1 Structure of the Human Eye58

2.1.2 Image Formation in the Eye60

2.1.3 Brightness Adaptation and Discrimination61

2.2 Light and the Electromagnetic Spectrum65

2.3 Image Sensing and Acquisition68

2.3.1 Image Acquisition Using a Single Sensor70

2.3.2 Image Acquisition Using Sensor Strips70

2.3.3 Image Acquisition Using Sensor Arrays72

2.3.4 A Simple Image Formation Model72

2.4 Image Sampling and Quantization74

2.4.1 Basic Concepts in Sampling and Quantization74

2.4.2 Representing Digital Images77

2.4.3 Spatial and Intensity Resolution81

2.4.4 Image Interpolation87

2.5 Some Basic Relationships between Pixels90

2.5.1 Neighbors of a Pixel90

2.5.2 Adjacency，Connectivity，Regions，and Boundaries90

2.5.3 Distance Measures93

2.6 An Introduction to the Mathematical Tools Used in Digital Image Processing94

2.6.1 Array versus Matrix Operations94

2.6.2 Linear versus Nonlinear Operations95

2.6.3 Arithmetic Operations96

2.6.4 Set and Logical Operations102

2.6.5 Spatial Operations107

2.6.6 Vector and Matrix Operations114

2.6.7 Image Transforms115

2.6.8 Probabilistic Methods118

Summary120

References and Further Reading120

Problems121

3 Intensity Transformations and Spatial Filtering126

3.1 Background127

3.1.1 The Basics of Intensity Transformations and Spatial Filtering127

3.1.2 About the Examples in This Chapter129

3.2 Some Basic Intensity Transformation Functions129

3.2.1 Image Negatives130

3.2.2 Log Transformations131

3.2.3 Power-Law（Gamma）Transformations132

3.2.4 Piecewise-Linear Transformation Functions137

3.3 Histogram Processing142

3.3.1 Histogram Equalization144

3.3.2 Histogram Matching（Specification）150

3.3.3 Local Histogram Processing161

3.3.4 Using Histogram Statistics for Image Enhancement161

3.4 Fundamentals of Spatial Filtering166

3.4.1 The Mechanics of Spatial Filtering167

3.4.2 Spatial Correlation and Convolution168

3.4.3 Vector Representation of Linear Filtering172

3.4.4 Generating Spatial Filter Masks173

3.5 Smoothing Spatial Filters174

3.5.1 Smoothing Linear Filters174

3.5.2 Order-Statistic（Nonlinear）Filters178

3.6 Sharpening Spatial Filters179

3.6.1 Foundation180

3.6.2 Using the Second Derivative for Image Sharpening—The Laplacian182

3.6.3 Unsharp Masking and Highboost Filtering184

3.6.4 Using First-Order Derivatives for（Nonlinear）Image Sharpening—The Gradient187

3.7 Combining Spatial Enhancement Methods191

3.8 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering195

3.8.1 Introduction195

3.8.2 Principles of Fuzzy Set Theory196

3.8.3 Using Fuzzy Sets200

3.8.4 Using Fuzzy Sets for Intensity Transformations208

3.8.5 Using Fuzzy Sets for Spatial Filtering211

Summary214

References and Further Reading214

Problems215

4 Filtering in the Frequency Domain221

4.1 Background222

4.1.1 A Brief History of the Fourier Series and Transform222

4.1.2 About the Examples in this Chapter223

4.2 Preliminary Concepts224

4.2.1 Complex Numbers224

4.2.2 Fourier Series225

4.2.3 Impulses and Their Sifting Property225

4.2.4 The Fourier Transform of Functions of One Continuous Variable227

4.2.5 Convolution231

4.3 Sampling and the Fourier Transform of Sampled Functions233

4.3.1 Sampling233

4.3.2 The Fourier Transform of Sampled Functions234

4.3.3 The Sampling Theorem235

4.3.4 Aliasing239

4.3.5 Function Reconstruction（Recovery）from Sampled Data241

4.4 The Discrete Fourier Transform（DFT）of One Variable242

4.4.1 Obtaining the DFT from the Continuous Transform of a Sampled Function243

4.4.2 Relationship Between the Sampling and Frequency Intervals245

4.5 Extension to Functions of Two Variables247

4.5.1 The 2-D Impulse and Its Sifting Property247

4.5.2 The 2-D Continuous Fourier Transform Pair248

4.5.3 Two-Dimensional Sampling and the 2-D Sampling Theorem249

4.5.4 Aliasing in Images250

4.5.5 The 2-D Discrete Fourier Transform and Its Inverse257

4.6 Some Properties of the 2-D Discrete Fourier Transform258

4.6.1 Relationships Between Spatial and Frequency Intervals258

4.6.2 Translation and Rotation258

4.6.3 Periodicity259

4.6.4 Symmetry Properties261

4.6.5 Fourier Spectrum and Phase Angle267

4.6.6 The 2-D Convolution Theorem271

4.6.7 Summary of 2-D Discrete Fourier Transform Properties275

4.7 The Basics of Filtering in the Frequency Domain277

4.7.1 Additional Characteristics of the Frequency Domain277

4.7.2 Frequency Domain Filtering Fundamentals279

4.7.3 Summary of Steps for Filtering in the Frequency Domain285

4.7.4 Correspondence Between Filtering in the Spatial and Frequency Domains285

4.8 Image Smoothing Using Frequency Domain Filters291

4.8.1 Ideal Lowpass Filters291

4.8.2 Butterworth Lowpass Filters295

4.8.3 Gaussian Lowpass Filters298

4.8.4 Additional Examples of Lowpass Filtering299

4.9 Image Sharpening Using Frequency Domain Filters302

4.9.1 Ideal Highpass Filters303

4.9.2 Butterworth Highpass Filters306

4.9.3 Gaussian Highpass Filters307

4.9.4 The Laplacian in the Frequency Domain308

4.9.5 Unsharp Masking，Highboost Filtering，and High-Frequency-Emphasis Filtering310

4.9.6 Homomorphic Filtering311

4.10 Selective Filtering316

4.10.1 Bandreject and Bandpass Filters316

4.10.2 Notch Filters316

4.11 Implementation320

4.11.1 Separability of the 2-D DFT320

4.11.2 Computing the IDFT Using a DFT Algorithm321

4.11.3 The Fast Fourier Transform （FFT）321

4.11.4 Some Comments on Filter Design325

Summary325

References and Further Reading326

Problems326

5 Image Restoration and Reconstruction333

5.1 A Model of the Image Degradation/Restoration Process334

5.2 Noise Models335

5.2.1 Spatial and Frequency Properties of Noise335

5.2.2 Some Important Noise Probability Density Functions336

5.2.3 Periodic Noise340

5.2.4 Estimation of Noise Parameters341

5.3 Restoration in the Presence of Noise Only—Spatial Filtering344

5.3.1 Mean Filters344

5.3.2 Order-Statistic Filters347

5.3.3 Adaptive Filters352

5.4 Periodic Noise Reduction by Frequency Domain Filtering357

5.4.1 Bandreject Filters357

5.4.2 Bandpass Filters358

5.4.3 Notch Filters359

5.4.4 Optimum Notch Filtering360

5.5 Linear，Position-Invariant Degradations365

5.6 Estimating the Degradation Function368

5.6.1 Estimation by Image Observation368

5.6.2 Estimation by Experimentation369

5.6.3 Estimation by Modeling369

5.7 Inverse Filtering373

5.8 Minimum Mean Square Error （Wiener） Filtering374

5.9 Constrained Least Squares Filtering379

5.10 Geometric Mean Filter383

5.11 Image Reconstruction from Projections384

5.11.1 Introduction384

5.11.2 Principles of Computed Tomography （CT）387

5.11.3 Projections and the Radon Transform390

5.11.4 The Fourier-Slice Theorem396

5.11.5 Reconstruction Using Parallel-Beam Filtered Backprojections397

5.11.6 Reconstruction Using Fan-Beam Filtered Backprojections403

Summary409

References and Further Reading410

Problems411

6 Color Image Processing416

6.1 Color Fundamentals417

6.2 Color Models423

6.2.1 The RGB Color Model424

6.2.2 The CMY and CMYK Color Models428

6.2.3 The HSI Color Model429

6.3 Pseudocolor Image Processing436

6.3.1 Intensity Slicing437

6.3.2 Intensity to Color Transformations440

6.4 Basics of Full-Color Image Processing446

6.5 Color Transformations448

6.5.1 Formulation448

6.5.2 Color Complements452

6.5.3 Color Slicing453

6.5.4 Tone and Color Corrections455

6.5.5 Histogram Processing460

6.6 Smoothing and Sharpening461

6.6.1 Color Image Smoothing461

6.6.2 Color Image Sharpening464

6.7 Image Segmentation Based on Color465

6.7.1 Segmentation in HSI Color Space465

6.7.2 Segmentation in RGB Vector Space467

6.7.3 Color Edge Detection469

6.8 Noise in Color Images473

6.9 Color Image Compression476

Summary477

References and Further Reading478

Problems478

7 Wavelets and Multiresolution Processing483

7.1 Background484

7.1.1 Image Pyramids485

7.1.2 Subband Coding488

7.1.3 The Haar Transform496

7.2 Multiresolution Expansions499

7.2.1 Series Expansions499

7.2.2 Scaling Functions501

7.2.3 Wavelet Functions505

7.3 Wavelet Transforms in One Dimension508

7.3.1 The Wavelet Series Expansions508

7.3.2 The Discrete Wavelet Transform510

7.3.3 The Continuous Wavelet Transform513

7.4 The Fast Wavelet Transform515

7.5 Wavelet Transforms in Two Dimensions523

7.6 Wavelet Packets532

Summary542

References and Further Reading542

Problems543

8 Image Compression547

8.1 Fundamentals548

8.1.1 Coding Redundancy550

8.1.2 Spatial and Temporal Redundancy551

8.1.3 Irrelevant Information552

8.1.4 Measuring Image Information553

8.1.5 Fidelity Criteria556

8.1.6 Image Compression Models558

8.1.7 Image Formats，Containers，and Compression Standards560

8.2 Some Basic Compression Methods564

8.2.1 Huffman Coding564

8.2.2 Golomb Coding566

8.2.3 Arithmetic Coding570

8.2.4 LZW Coding573

8.2.5 Run-Length Coding575

8.2.6 Symbol-Based Coding581

8.2.7 Bit-Plane Coding584

8.2.8 Block Transform Coding588

8.2.9 Predictive Coding606

8.2.10 Wavelet Coding626

8.3 Digital Image Watermarking636

Summary643

References and Further Reading644

Problems645

9 Morphological Image Processing649

9.1 Preliminaries650

9.2 Erosion and Dilation652

9.2.1 Erosion653

9.2.2 Dilation655

9.2.3 Duality657

9.3 Opening and Closing657

9.4 The Hit-or-Miss Transformation662

9.5 Some Basic Morphological Algorithms664

9.5.1 Boundary Extraction664

9.5.2 Hole Filling665

9.5.3 Extraction of Connected Components667

9.5.4 Convex Hull669

9.5.5 Thinning671

9.5.6 Thickening672

9.5.7 Skeletons673

9.5.8 Pruning676

9.5.9 Morphological Reconstruction678

9.5.10 Summary of Morphological Operations on Binary Images684

9.6 Gray-Scale Morphology687

9.6.1 Erosion and Dilation688

9.6.2 Opening and Closing690

9.6.3 Some Basic Gray-Scale Morphological Algorithms692

9.6.4 Gray-Scale Morphological Reconstruction698

Summary701

References and Further Reading701

Problems702

10 Image Segmentation711

10.1 Fundamentals712

10.2 Point，Line，and Edge Detection714

10.2.1 Background714

10.2.2 Detection of Isolated Points718

10.2.3 Line Detection719

10.2.4 Edge Models722

10.2.5 Basic Edge Detection728

10.2.6 More Advanced Techniques for Edge Detection736

10.2.7 Edge Linking and Boundary Detection747

10.3 Thresholding760

10.3.1 Foundation760

10.3.2 Basic Global Thresholding763

10.3.3 Optimum Global Thresholding Using Otsu’s Method764

10.3.4 Using Image Smoothing to Improve Global Thresholding769

10.3.5 Using Edges to Improve Global Thresholding771

10.3.6 Multiple Thresholds774

10.3.7 Variable Thresholding778

10.3.8 Multivariable Thresholding783

10.4 Region-Based Segmentation785

10.4.1 Region Growing785

10.4.2 Region Splitting and Merging788

10.5 Segmentation Using Morphological Watersheds791

10.5.1 Background791

10.5.2 Dam Construction794

10.5.3 Watershed Segmentation Algorithm796

10.5.4 The Use of Markers798

10.6 The Use of Motion in Segmentation800

10.6.1 Spatial Techniques800

10.6.2 Frequency Domain Techniques804

Summary807

References and Further Reading807

Problems809

11 Representation and Description817

11.1 Representation818

11.1.1 Boundary（Border）Following818

11.1.2 Chain Codes820

11.1.3 Polygonal Approximations Using Minimum-Perimeter Polygons823

11.1.4 Other Polygonal Approximation Approaches829

11.1.5 Signatures830

11.1.6 Boundary Segments832

11.1.7 Skeletons834

11.2 Boundary Descriptors837

11.2.1 Some Simple Descriptors837

11.2.2 Shape Numbers838

11.2.3 Fourier Descriptors840

11.2.4 Statistical Moments843

11.3 Regional Descriptors844

11.3.1 Some Simple Descriptors844

11.3.2 Topological Descriptors845

11.3.3 Texture849

11.3.4 Moment Invariants861

11.4 Use of Principal Components for Description864

11.5 Relational Descriptors874

Summary878

References and Further Reading878

Problems879

12 Object Recognition883

12.1 Patterns and Pattern Classes883

12.2 Recognition Based on Decision-Theoretic Methods888

12.2.1 Matching888

12.2.2 Optimum Statistical Classifiers894

12.2.3 Neural Networks904

12.3 Structural Methods925

12.3.1 Matching Shape Numbers925

12.3.2 String Matching926

Summary928

References and Further Reading928

Problems929

Appendix A932

Bibliography937

Index965