Digital Image Processing 3rd Edition

Cover......Page 1
Title Page......Page 2
Copyright Page......Page 3
Contents......Page 6
Preface......Page 16
Acknowledgments......Page 20
The Book Web Site......Page 21
About the Authors......Page 22
1.1 What Is Digital Image Processing?......Page 24
1.2 The Origins of Digital Image Processing......Page 26
1.3 Examples of Fields that Use Digital Image Processing......Page 30
1.3.1 Gamma-Ray Imaging......Page 31
1.3.2 X-Ray Imaging......Page 32
1.3.3 Imaging in the Ultraviolet Band......Page 34
1.3.4 Imaging in the Visible and Infrared Bands......Page 35
1.3.5 Imaging in the Microwave Band......Page 41
1.3.7 Examples in which Other Imaging Modalities Are Used......Page 43
1.4 Fundamental Steps in Digital Image Processing......Page 48
1.5 Components of an Image Processing System......Page 51
References and Further Reading......Page 54
2 Digital Image Fundamentals......Page 58
2.1.1 Structure of the Human Eye......Page 59
2.1.2 Image Formation in the Eye......Page 61
2.1.3 Brightness Adaptation and Discrimination......Page 62
2.2 Light and the Electromagnetic Spectrum......Page 66
2.3 Image Sensing and Acquisition......Page 69
2.3.2 Image Acquisition Using Sensor Strips......Page 71
2.3.4 A Simple Image Formation Model......Page 73
2.4.1 Basic Concepts in Sampling and Quantization......Page 75
2.4.2 Representing Digital Images......Page 78
2.4.3 Spatial and Intensity Resolution......Page 82
2.4.4 Image Interpolation......Page 88
2.5.2 Adjacency, Connectivity, Regions, and Boundaries......Page 91
2.5.3 Distance Measures......Page 94
2.6.1 Array versus Matrix Operations......Page 95
2.6.2 Linear versus Nonlinear Operations......Page 96
2.6.3 Arithmetic Operations......Page 97
2.6.4 Set and Logical Operations......Page 103
2.6.5 Spatial Operations......Page 108
2.6.6 Vector and Matrix Operations......Page 115
2.6.7 Image Transforms......Page 116
2.6.8 Probabilistic Methods......Page 119
References and Further Reading......Page 121
Problems......Page 122
3 Intensity Transformations and Spatial Filtering......Page 127
3.1.1 The Basics of Intensity Transformations and Spatial Filtering......Page 128
3.2 Some Basic Intensity Transformation Functions......Page 130
3.2.1 Image Negatives......Page 131
3.2.2 Log Transformations......Page 132
3.2.3 Power-Law (Gamma) Transformations......Page 133
3.2.4 Piecewise-Linear Transformation Functions......Page 138
3.3 Histogram Processing......Page 143
3.3.1 Histogram Equalization......Page 145
3.3.2 Histogram Matching (Specification)......Page 151
3.3.4 Using Histogram Statistics for Image Enhancement......Page 162
3.4 Fundamentals of Spatial Filtering......Page 167
3.4.1 The Mechanics of Spatial Filtering......Page 168
3.4.2 Spatial Correlation and Convolution......Page 169
3.4.3 Vector Representation of Linear Filtering......Page 173
3.4.4 Generating Spatial Filter Masks......Page 174
3.5.1 Smoothing Linear Filters......Page 175
3.5.2 Order-Statistic (Nonlinear) Filters......Page 179
3.6 Sharpening Spatial Filters......Page 180
3.6.1 Foundation......Page 181
3.6.2 Using the Second Derivative for Image Sharpening—The Laplacian......Page 183
3.6.3 Unsharp Masking and Highboost Filtering......Page 185
3.6.4 Using First-Order Derivatives for (Nonlinear) Image Sharpening—The Gradient......Page 188
3.7 Combining Spatial Enhancement Methods......Page 192
3.8.1 Introduction......Page 196
3.8.2 Principles of Fuzzy Set Theory......Page 197
3.8.3 Using Fuzzy Sets......Page 201
3.8.4 Using Fuzzy Sets for Intensity Transformations......Page 209
3.8.5 Using Fuzzy Sets for Spatial Filtering......Page 212
References and Further Reading......Page 215
Problems......Page 216
4 Filtering in the Frequency Domain......Page 222
4.1.1 A Brief History of the Fourier Series and Transform......Page 223
4.1.2 About the Examples in this Chapter......Page 224
4.2.1 Complex Numbers......Page 225
4.2.3 Impulses and Their Sifting Property......Page 226
4.2.4 The Fourier Transform of Functions of One Continuous Variable......Page 228
4.2.5 Convolution......Page 232
4.3.1 Sampling......Page 234
4.3.2 The Fourier Transform of Sampled Functions......Page 235
4.3.3 The Sampling Theorem......Page 236
4.3.4 Aliasing......Page 240
4.3.5 Function Reconstruction (Recovery) from Sampled Data......Page 242
4.4 The Discrete Fourier Transform (DFT) of One Variable......Page 243
4.4.1 Obtaining the DFT from the Continuous Transform of a Sampled Function......Page 244
4.4.2 Relationship Between the Sampling and Frequency Intervals......Page 246
4.5.1 The 2-D Impulse and Its Sifting Property......Page 248
4.5.2 The 2-D Continuous Fourier Transform Pair......Page 249
4.5.3 Two-Dimensional Sampling and the 2-D Sampling Theorem......Page 250
4.5.4 Aliasing in Images......Page 251
4.5.5 The 2-D Discrete Fourier Transform and Its Inverse......Page 258
4.6.2 Translation and Rotation......Page 259
4.6.3 Periodicity......Page 260
4.6.4 Symmetry Properties......Page 262
4.6.5 Fourier Spectrum and Phase Angle......Page 268
4.6.6 The 2-D Convolution Theorem......Page 272
4.6.7 Summary of 2-D Discrete Fourier Transform Properties......Page 276
4.7.1 Additional Characteristics of the Frequency Domain......Page 278
4.7.2 Frequency Domain Filtering Fundamentals......Page 280
4.7.4 Correspondence Between Filtering in the Spatial and Frequency Domains......Page 286
4.8.1 Ideal Lowpass Filters......Page 292
4.8.2 Butterworth Lowpass Filters......Page 296
4.8.3 Gaussian Lowpass Filters......Page 299
4.8.4 Additional Examples of Lowpass Filtering......Page 300
4.9 Image Sharpening Using Frequency Domain Filters......Page 303
4.9.1 Ideal Highpass Filters......Page 304
4.9.2 Butterworth Highpass Filters......Page 307
4.9.3 Gaussian Highpass Filters......Page 308
4.9.4 The Laplacian in the Frequency Domain......Page 309
4.9.5 Unsharp Masking, Highboost Filtering, and High-Frequency- Emphasis Filtering......Page 311
4.9.6 Homomorphic Filtering......Page 312
4.10.2 Notch Filters......Page 317
4.11.1 Separability of the 2-D DFT......Page 321
4.11.3 The Fast Fourier Transform (FFT)......Page 322
Summary......Page 326
Problems......Page 327
5 Image Restoration and Reconstruction......Page 334
5.1 A Model of the Image Degradation/Restoration Process......Page 335
5.2.1 Spatial and Frequency Properties of Noise......Page 336
5.2.2 Some Important Noise Probability Density Functions......Page 337
5.2.3 Periodic Noise......Page 341
5.2.4 Estimation of Noise Parameters......Page 342
5.3.1 Mean Filters......Page 345
5.3.2 Order-Statistic Filters......Page 348
5.3.3 Adaptive Filters......Page 353
5.4.1 Bandreject Filters......Page 358
5.4.2 Bandpass Filters......Page 359
5.4.3 Notch Filters......Page 360
5.4.4 Optimum Notch Filtering......Page 361
5.5 Linear, Position-Invariant Degradations......Page 366
5.6.1 Estimation by Image Observation......Page 369
5.6.3 Estimation by Modeling......Page 370
5.7 Inverse Filtering......Page 374
5.8 Minimum Mean Square Error (Wiener) Filtering......Page 375
5.9 Constrained Least Squares Filtering......Page 380
5.10 Geometric Mean Filter......Page 384
5.11.1 Introduction......Page 385
5.11.2 Principles of Computed Tomography (CT)......Page 388
5.11.3 Projections and the Radon Transform......Page 391
5.11.4 The Fourier-Slice Theorem......Page 397
5.11.5 Reconstruction Using Parallel-Beam Filtered Backprojections......Page 398
5.11.6 Reconstruction Using Fan-Beam Filtered Backprojections......Page 404
Summary......Page 410
References and Further Reading......Page 411
Problems......Page 412
6 Color Image Processing......Page 417
6.1 Color Fundamentals......Page 418
6.2 Color Models......Page 424
6.2.1 The RGB Color Model......Page 425
6.2.2 The CMY and CMYK Color Models......Page 429
6.2.3 The HSI Color Model......Page 430
6.3 Pseudocolor Image Processing......Page 437
6.3.1 Intensity Slicing......Page 438
6.3.2 Intensity to Color Transformations......Page 441
6.4 Basics of Full-Color Image Processing......Page 447
6.5.1 Formulation......Page 449
6.5.2 Color Complements......Page 453
6.5.3 Color Slicing......Page 454
6.5.4 Tone and Color Corrections......Page 456
6.5.5 Histogram Processing......Page 461
6.6.1 Color Image Smoothing......Page 462
6.6.2 Color Image Sharpening......Page 465
6.7.1 Segmentation in HSI Color Space......Page 466
6.7.2 Segmentation in RGB Vector Space......Page 468
6.7.3 Color Edge Detection......Page 470
6.8 Noise in Color Images......Page 474
6.9 Color Image Compression......Page 477
Summary......Page 478
Problems......Page 479
7 Wavelets and Multiresolution Processing......Page 484
7.1 Background......Page 485
7.1.1 Image Pyramids......Page 486
7.1.2 Subband Coding......Page 489
7.1.3 The Haar Transform......Page 497
7.2.1 Series Expansions......Page 500
7.2.2 Scaling Functions......Page 502
7.2.3 Wavelet Functions......Page 506
7.3.1 The Wavelet Series Expansions......Page 509
7.3.2 The Discrete Wavelet Transform......Page 511
7.3.3 The Continuous Wavelet Transform......Page 514
7.4 The Fast Wavelet Transform......Page 516
7.5 Wavelet Transforms in Two Dimensions......Page 524
7.6 Wavelet Packets......Page 533
References and Further Reading......Page 543
Problems......Page 544
8 Image Compression......Page 548
8.1 Fundamentals......Page 549
8.1.1 Coding Redundancy......Page 551
8.1.2 Spatial and Temporal Redundancy......Page 552
8.1.3 Irrelevant Information......Page 553
8.1.4 Measuring Image Information......Page 554
8.1.5 Fidelity Criteria......Page 557
8.1.6 Image Compression Models......Page 559
8.1.7 Image Formats, Containers, and Compression Standards......Page 561
8.2.1 Huffman Coding......Page 565
8.2.2 Golomb Coding......Page 567
8.2.3 Arithmetic Coding......Page 571
8.2.4 LZW Coding......Page 574
8.2.5 Run-Length Coding......Page 576
8.2.6 Symbol-Based Coding......Page 582
8.2.7 Bit-Plane Coding......Page 585
8.2.8 Block Transform Coding......Page 589
8.2.9 Predictive Coding......Page 607
8.2.10 Wavelet Coding......Page 627
8.3 Digital Image Watermarking......Page 637
Summary......Page 644
References and Further Reading......Page 645
Problems......Page 646
9 Morphological Image Processing......Page 650
9.1 Preliminaries......Page 651
9.2 Erosion and Dilation......Page 653
9.2.1 Erosion......Page 654
9.2.2 Dilation......Page 656
9.3 Opening and Closing......Page 658
9.4 The Hit-or-Miss Transformation......Page 663
9.5.1 Boundary Extraction......Page 665
9.5.2 Hole Filling......Page 666
9.5.3 Extraction of Connected Components......Page 668
9.5.4 Convex Hull......Page 670
9.5.5 Thinning......Page 672
9.5.6 Thickening......Page 673
9.5.7 Skeletons......Page 674
9.5.8 Pruning......Page 677
9.5.9 Morphological Reconstruction......Page 679
9.5.10 Summary of Morphological Operations on Binary Images......Page 685
9.6 Gray-Scale Morphology......Page 688
9.6.1 Erosion and Dilation......Page 689
9.6.2 Opening and Closing......Page 691
9.6.3 Some Basic Gray-Scale Morphological Algorithms......Page 693
9.6.4 Gray-Scale Morphological Reconstruction......Page 699
References and Further Reading......Page 702
Problems......Page 703
10 Image Segmentation......Page 712
10.1 Fundamentals......Page 713
10.2.1 Background......Page 715
10.2.2 Detection of Isolated Points......Page 719
10.2.3 Line Detection......Page 720
10.2.4 Edge Models......Page 723
10.2.5 Basic Edge Detection......Page 729
10.2.6 More Advanced Techniques for Edge Detection......Page 737
10.2.7 Edge Linking and Boundary Detection......Page 748
10.3.1 Foundation......Page 761
10.3.2 Basic Global Thresholding......Page 764
10.3.3 Optimum Global Thresholding Using Otsu’s Method......Page 765
10.3.4 Using Image Smoothing to Improve Global Thresholding......Page 770
10.3.5 Using Edges to Improve Global Thresholding......Page 772
10.3.6 Multiple Thresholds......Page 775
10.3.7 Variable Thresholding......Page 779
10.3.8 Multivariable Thresholding......Page 784
10.4.1 Region Growing......Page 786
10.4.2 Region Splitting and Merging......Page 789
10.5.1 Background......Page 792
10.5.2 Dam Construction......Page 795
10.5.3 Watershed Segmentation Algorithm......Page 797
10.5.4 The Use of Markers......Page 799
10.6.1 Spatial Techniques......Page 801
10.6.2 Frequency Domain Techniques......Page 805
References and Further Reading......Page 808
Problems......Page 810
11 Representation and Description......Page 818
11.1.1 Boundary (Border) Following......Page 819
11.1.2 Chain Codes......Page 821
11.1.3 Polygonal Approximations Using Minimum-Perimeter Polygons......Page 824
11.1.4 Other Polygonal Approximation Approaches......Page 830
11.1.5 Signatures......Page 831
11.1.6 Boundary Segments......Page 833
11.1.7 Skeletons......Page 835
11.2.1 Some Simple Descriptors......Page 838
11.2.2 Shape Numbers......Page 839
11.2.3 Fourier Descriptors......Page 841
11.2.4 Statistical Moments......Page 844
11.3.1 Some Simple Descriptors......Page 845
11.3.2 Topological Descriptors......Page 846
11.3.3 Texture......Page 850
11.3.4 Moment Invariants......Page 862
11.4 Use of Principal Components for Description......Page 865
11.5 Relational Descriptors......Page 875
References and Further Reading......Page 879
Problems......Page 880
12.1 Patterns and Pattern Classes......Page 884
12.2.1 Matching......Page 889
12.2.2 Optimum Statistical Classifiers......Page 895
12.2.3 Neural Networks......Page 905
12.3.1 Matching Shape Numbers......Page 926
12.3.2 String Matching......Page 927
References and Further Reading......Page 929
Problems......Page 930
Appendix A......Page 933
Bibliography......Page 938
C......Page 966
D......Page 967
E......Page 968
F......Page 969
I......Page 970
M......Page 971
N......Page 972
Q......Page 973
S......Page 974
T......Page 975
W......Page 976
Z......Page 977