数字图像处理 MATLAB版 第2版 英文版【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

数字图像处理 MATLAB版 第2版 英文版PDF格式电子书版下载

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1 Introduction1

Preview1

1.1 Background1

1.2 What Is Digital Image Processing？2

1.3 Background on MATLAB and the Image Processing Toolbox4

1.4 Areas of Image Processing Covered in the Book4

1.5 The Book Web Site6

1.6 Notation7

1.7 Fundamentals7

1.7.1 The MATLAB Desktop7

1.7.2 Using the MATLAB Editor/Debugger9

1.7.3 Getting Help9

1.7.4 Saving and Retrieving Work Session Data10

1.7.5 Digital Image Representation11

1.7.6 Image I/O and Display13

1.7.7 Classes and Image Types14

1.7.8 M-Function Programming17

1.8 How References Are Organized in the Book33

Summary33

2 Intensity Transformations and Spatial Filtering34

Preview34

2.1 Background34

2.2 Intensity Transformation Functions35

2.2.1 Functions imadjust and stretchlim36

2.2.2 Logarithmic and Contrast-Stretching Transformations38

2.2.3 Specifying Arbitrary Intensity Transformations40

2.2.4 Some Utility M-functions for Intensity Transformations41

2.3 Histogram Processing and Function Plotting47

2.3.1 Generating and Plotting Image Histograms48

2.3.2 Histogram Equalization53

2.3.3 Histogram Matching （Specification）56

2.3.4 Function adapthisteq61

2.4 Spatial Filtering63

2.4.1 Linear Spatial Filtering63

2.4.2 Nonlinear Spatial Filtering71

2.5 Image Processing Toolbox Standard Spatial Filters74

2.5.1 Linear Spatial Filters74

2.5.2 Nonlinear Spatial Filters78

2.6 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering82

2.6.1 Background82

2.6.2 Introduction to Fuzzy Sets82

2.6.3 Using Fuzzy Sets87

2.6.4 A Set of Custom Fuzzy M-functions94

2.6.5 Using Fuzzy Sets for Intensity Transformations109

2.6.6 Using Fuzzy Sets for Spatial Filtering112

Summary117

3 Filtering in the Frequency Domain118

Preview118

3.1 The 2-D Discrete Fourier Transform118

3.2 Computing and Visualizing the 2-D DFT in MATLAB122

3.3 Filtering in the Frequency Domain126

3.3.1 Fundamentals127

3.3.2 Basic Steps in DFT Filtering132

3.3.3 An M-function for Filtering in the Frequency Domain133

3.4 Obtaining Frequency Domain Filters from Spatial Filters134

3.5 Generating Filters Directly in the Frequency Domain139

3.5.1 Creating Meshgrid Arrays for Use in Implementing Filters in the Frequency Domain140

3.5.2 Lowpass （Smoothing） Frequency Domain Filters141

3.5.3 Wireframe and Surface Plotting144

3.6 Highpass （Sharpening） Frequency Domain Filters148

3.6.1 A Function for Highpass Filtering148

3.6.2 High-Frequency Emphasis Filtering151

3.7 Selective Filtering153

3.7.1 Bandreject and Bandpass Filters153

3.7.2 Notchreject and Notchpass Filters156

Summary162

4 Image Restoration and Reconstruction163

Preview163

4.1 A Model of the Image Degradation/Restoration Process164

4.2 Noise Models165

4.2.1 Adding Noise to Images with Function imnoise165

4.2.2 Generating Spatial Random Noise with a Specified Distribution166

4.2.3 Periodic Noise174

4.2.4 Estimating Noise Parameters178

4.3 Restoration in the Presence of Noise Only—Spatial Filtering183

4.3.1 Spatial Noise Filters183

4.3.2 Adaptive Spatial Filters187

4.4 Periodic Noise Reduction Using Frequency Domain Filtering190

4.5 Modeling the Degradation Function191

4.6 Direct Inverse Filtering194

4.7 Wiener Filtering194

4.8 Constrained Least Squares （Regularized） Filtering198

4.9 Iterative Nonlinear Restoration Using the Lucy-Richardson Algorithm200

4.10 Blind Deconvolution204

4.11 Image Reconstruction from Projections205

4.11.1 Background206

4.11.2 Parallel-Beam Projections and the Radon Transform208

4.11.3 The Fourier Slice Theorem and Filtered Backprojections211

4.11.4 Filter Implementation212

4.11.5 Reconstruction Using Fan-Beam Filtered Backprojections213

4.11.6 Function radon214

4.11.7 Function iradon217

4.11.8 Working with Fan-Beam Data222

Summary231

5 Geometric Transformations and Image Registration232

Preview232

5.1 Transforming Points232

5.2 Affine Transformations237

5.3 Projective Transformations241

5.4 Applying Geometric Transformations to Images242

5.5 Image Coordinate Systems in MATLAB245

5.5.1 Output Image Location247

5.5.2 Controlling the Output Grid251

5.6 Image Interpolation253

5.6.1 Interpolation in Two Dimensions256

5.6.2 Comparing Interpolation Methods256

5.7 Image Registration259

5.7.1 Registration Process260

5.7.2 Manual Feature Selection and Matching Using cpselect260

5.7.3 Inferring Transformation Parameters Using cp2tform261

5.7.4 Visualizing Aligned Images261

5.7.5 Area-Based Registration265

5.7.6 Automatic Feature-Based Registration270

Summary271

6 Color Image Processing272

Preview272

6.1 Color Image Representation in MATLAB272

6.1.1 RGB Images272

6.1.2 Indexed Images275

6.1.3 Functions for Manipulating RGB and Indexed Images277

6.2 Converting Between Color Spaces282

6.2.1 NTSC Color Space282

6.2.2 The YCbCr Color Space283

6.2.3 The HSV Color Space283

6.2.4 The CMY and CMYK Color Spaces284

6.2.5 The HSI Color Space285

6.2.6 Device-Independent Color Spaces294

6.3 The Basics of Color Image Processing303

6.4 Color Transformations304

6.5 Spatial Filtering of Color Images314

6.5.1 Color Image Smoothing314

6.5.2 Color Image Sharpening319

6.6 Working Directly in RGB Vector Space320

6.6.1 Color Edge Detection Using the Gradient320

6.6.2 Image Segmentation in RGB Vector Space326

Summary330

7 Wavelets331

Preview331

7.1 Background331

7.2 The Fast Wavelet Transform334

7.2.1 FWTs Using the Wavelet Toolbox335

7.2.2 FWTs without the Wavelet Toolbox341

7.3 Working with Wavelet Decomposition Structures350

7.3.1 Editing Wavelet Decomposition Coefficients without the Wavelet Toolbox353

7.3.2 Displaying Wavelet Decomposition Coefficients358

7.4 The Inverse Fast Wavelet Transform362

7.5 Wavelets in Image Processing368

Summary373

8 Image Compression374

Preview374

8.1 Background375

8.2 Coding Redundancy378

8.2.1 Huffman Codes381

8.2.2 Huffman Encoding387

8.2.3 Huffman Decoding393

8.3 Spatial Redundancy400

8.4 Irrelevant Information407

8.5 JPEG Compression410

8.5.1 JPEG410

8.5.2 JPEG 2000418

8.6 Video Compression426

8.6.1 MATLAB Image Sequences and Movies427

8.6.2 Temporal Redundancy and Motion Compensation430

Summary439

9 Morphological Image Processing440

Preview440

9.1 Preliminaries441

9.1.1 Some Basic Concepts from Set Theory441

9.1.2 Binary Images， Sets， and Logical Operators443

9.2 Dilation and Erosion444

9.2.1 Dilation444

9.2.2 Structuring Element Decomposition447

9.2.3 The strel Function448

9.2.4 Erosion451

9.3 Combining Dilation and Erosion454

9.3.1 Opening and Closing454

9.3.2 The Hit-or-Miss Transformation457

9.3.3 Using Lookup Tables460

9.3.4 Function bwmorph465

9.4 Labeling Connected Components468

9.5 Morphological Reconstruction472

9.5.1 Opening by Reconstruction472

9.5.2 Filling Holes474

9.5.3 Clearing Border Objects475

9.6 Gray-Scale Morphology475

9.6.1 Dilation and Erosion475

9.6.2 Opening and Closing478

9.6.3 Reconstruction484

Summary488

10 Image Segmentation489

Preview489

10.1 Point， Line， and Edge Detection490

10.1.1 Point Detection490

10.1.2 Line Detection492

10.1.3 Edge Detection Using Function edge495

10.2 Line Detection Using the Hough Transform503

10.2.1 Background505

10.2.2 Toolbox Hough Functions506

10.3 Thresholding511

10.3.1 Foundation511

10.3.2 Basic Global Thresholding513

10.3.3 Optimum Global Thresholding Using Otsu’s Method515

10.3.4 Using Image Smoothing to Improve Global Thresholding519

10.3.5 Using Edges to Improve Global Thresholding521

10.3.6 Variable Thresholding Based on Local Statistics525

10.3.7 Image Thresholding Using Moving Averages529

10.4 Region-Based Segmentation532

10.4.1 Basic Formulation532

10.4.2 Region Growing532

10.4.3 Region Splitting and Merging536

10.5 Segmentation Using the Watershed Transform542

10.5.1 Watershed Segmentation Using the Distance Transform543

10.5.2 Watershed Segmentation Using Gradients545

10.5.3 Marker-Controlled Watershed Segmentation547

Summary550

11 Representation and Description551

Preview551

11.1 Background551

11.1.1 Functions for Extracting Regions and Their Boundaries552

11.1.2 Some Additional MATLAB and Toolbox Functions Used in This Chapter557

11.1.3 Some Basic Utility M-Functions558

11.2 Representation560

11.2.1 Chain Codes560

11.2.2 Polygonal Approximations Using Minimum-Perimeter Polygons564

11.2.3 Signatures573

11.2.4 Boundary Segments576

11.2.5 Skeletons577

11.3 Boundary Descriptors579

11.3.1 Some Simple Descriptors579

11.3.2 Shape Numbers580

11.3.3 Fourier Descriptors581

11.3.4 Statistical Moments586

11.3.5 Corners587

11.4 Regional Descriptors595

11.4.1 Function regionprops596

11.4.2 Texture598

11.4.3 Moment Invariants610

11.5 Using Principal Components for Description615

Summary626

Appendix A M-Function Summary628

Appendix B ICE and MATLAB Graphical User Interfaces643

AppendixC Additional Custom M -functions669

Bibliography725

Index729