Digital Image Processing

Cover......Page 1
Contents......Page 7
Preface......Page 15
1.1 What Is Digital Image Processing?......Page 19
1.2 The Origins of Digital Image Processing......Page 21
1.3 Examples of Fields that Use Digital Image Processing......Page 25
1.3.1 Gamma-Ray Imaging......Page 26
1.3.2 X-ray Imaging......Page 27
1.3.3 Imaging in the Ultraviolet Band......Page 29
1.3.4 Imaging in the Visible and Infrared Bands......Page 30
1.3.5 Imaging in the Microwave Band......Page 36
1.3.7 Examples in which Other Imaging Modalities Are Used......Page 38
1.4 Fundamental Steps in Digital Image Processing......Page 43
1.5 Components of an Image Processing System......Page 46
Summary......Page 48
References and Further Reading......Page 49
2.1 Elements of Visual Perception......Page 52
2.1.1 Structure of the Human Eye......Page 53
2.1.2 Image Formation in the Eye......Page 55
2.1.3 Brightness Adaptation and Discrimination......Page 56
2.2 Light and the Electromagnetic Spectrum......Page 60
2.3 Image Sensing and Acquisition......Page 63
2.3.1 Image Acquisition Using a Single Sensor......Page 65
2.3.2 Image Acquisition Using Sensor Strips......Page 66
2.3.3 Image Acquisition Using Sensor Arrays......Page 67
2.3.4 A Simple Image Formation Model......Page 68
2.4.1 Basic Concepts in Sampling and Quantization......Page 70
2.4.2 Representing Digital Images......Page 72
2.4.3 Spatial and Gray-Level Resolution......Page 75
2.4.4 Aliasing and Moiré Patterns......Page 80
2.4.5 Zooming and Shrinking Digital Images......Page 82
2.5.2 Adjacency, Connectivity, Regions, and Boundaries......Page 84
2.5.3 Distance Measures......Page 86
2.5.4 Image Operations on a Pixel Basis......Page 87
References and Further Reading......Page 88
Problems......Page 89
3 - Image Enhancement in the Spatial Domain......Page 93
3.1 Background......Page 94
3.2.1 Image Negatives......Page 96
3.2.2 Log Transformations......Page 97
3.2.3 Power-Law Transformations......Page 98
3.2.4 Piecewise-Linear Transformation Functions......Page 103
3.3 Histogram Processing......Page 106
3.3.1 Histogram Equalization......Page 109
3.3.2 Histogram Matching (Specification)......Page 112
3.3.4 Use of Histogram Statistics for Image Enhancement......Page 121
3.4 Enhancement Using Arithmetic/Logic Operations......Page 126
3.4.1 Image Subtraction......Page 128
3.4.2 Image Averaging......Page 130
3.5 Basics of Spatial Filtering......Page 134
3.6.1 Smoothing Linear Filters......Page 137
3.6.2 Order-Statistics Filters......Page 141
3.7.1 Foundation......Page 143
3.7.2 Use of Second Derivatives for Enhancement–The Laplacian......Page 146
3.7.3 Use of First Derivatives for Enhancement—The Gradient......Page 152
3.8 Combining Spatial Enhancement Methods......Page 155
Summary......Page 159
Problems......Page 160
4 - Image Enhancement in the Frequency Domain......Page 165
4.1 Background......Page 166
4.2 Introduction to the Fourier Transform and the Frequency Domain......Page 167
4.2.1 The One-Dimensional Fourier Transform and its Inverse......Page 168
4.2.2 The Two-Dimensional DFT and Its Inverse......Page 172
4.2.3 Filtering in the Frequency Domain......Page 174
4.2.4 Correspondence between Filtering in the Spatial and Frequency Domains......Page 179
4.3.1 Ideal Lowpass Filters......Page 185
4.3.2 Butterworth Lowpass Filters......Page 191
4.3.3 Gaussian Lowpass Fifters......Page 193
4.3.4 Additional Examples of Lowpass Filtering......Page 196
4.4 Sharpening Frequency Domain Filters......Page 198
4.4.1 Ideal Highpass Filters......Page 200
4.4.2 Butterworth Highpass Filters......Page 201
4.4.3 Gaussian Highpass Filters......Page 202
4.4.4 The Laplacian in the Frequency Domain......Page 203
4.4.5 Unsharp Masking, High-Boost Filtering, and High-Frequency Emphasis Filtering......Page 205
4.5 Homomorphic Filtering......Page 209
4.6.1 Some Additional Properties of the 2-D Fourier Transform......Page 212
4.6.2 Computing the Inverse Fourier Transform Using a Forward Transform Algorithm......Page 216
4.6.3 More on Periodicity: the Need for Padding......Page 217
4.6.4 The Convolution and Correlation Theorems......Page 223
4.6.6 The Fast Fourier Transform......Page 226
4.6.7 Some Comments on Filter Design......Page 231
References......Page 232
Problems......Page 233
5 - Image Restoration......Page 238
5.1 A Model of the Image Degradation/Restoration Process......Page 239
5.2.2 Some Inportant Noise Probability Density Functions......Page 240
5.2.4 Estimation of Noise Parameters......Page 245
5.3 Restoration in the Presence of Noise Only-Spatial Filtering......Page 248
5.3.1 Mean Filters......Page 249
5.3.2 Order-Statistics Filters......Page 251
5.3.3 Adaptive Filters......Page 255
5.4 Periodic Noise Reduction by Frequency Domain Filtering......Page 261
5.4.1 Bandreject Filters......Page 262
5.4.2 Bandpass Filters......Page 263
5.4.3 Notch Filters......Page 264
5.4.4 Optimum Notch Filtering......Page 266
5.5 Linear, Position-Invariant Degradations......Page 272
5.6.1 Estimation by Image Observation......Page 274
5.6.2 Estimation by Experimentation......Page 275
5.6.3 Estimation by ModeIing......Page 276
5.7 Inverse Filtering......Page 279
5.8 Minimum Mean Square Error (Wiener) Filtering......Page 280
5.9 Constrained Least Squares Filtering......Page 284
5.11 Geometric Transformations......Page 288
5.11.1 Spatial Transformations......Page 289
5.11.2 Gray-Level Interpolation......Page 290
Summary......Page 294
References and Further Reading......Page 295
Problems......Page 296
6 - Color Image Processing......Page 300
6.1 Color Fundamentals......Page 301
6.2 Color Models......Page 307
6.2.1 The RGB Color Model......Page 308
6.2.2 The CMY and CMYK Color Models......Page 312
6.2.3 The HSI Color Model......Page 313
6.3 Pseudocolor Image Processing......Page 320
6.3.1 Intensity Slicing......Page 321
6.3.2 Gray Level to Color Transformations......Page 326
6.4 Basics of Full-Color Image Processing......Page 331
6.5.1 Formulation......Page 333
6.5.2 Color Complements......Page 336
6.5.3 Color Slicing......Page 338
6.5.4 Tone and Color Corrections......Page 340
6.5.5 Histogram Processing......Page 344
6.6 Smoothing and Sharpening......Page 345
6.6.1 Color Image Smoothing......Page 346
6.6.2 Color Image Sharpening......Page 348
6.7.1 Segmentation in HSI Color Space......Page 349
6.7.2 Segmentation in RGB Vector Space......Page 351
6.7.3 Color Edge Detection......Page 353
6.8 Noise in Color Images......Page 357
6.9 Color Image Compression......Page 360
Summary......Page 361
Problems......Page 362
7 - Wavelets and Multiresolution Processing......Page 367
7.1 Background......Page 368
7.1.1 Image Pyramids......Page 369
7.1.2 Subband Coding......Page 372
7.1.3 The Haar Transform......Page 378
7.2 Multiresolution Expansions......Page 381
7.2.1 Series Expansions......Page 382
7.2.2 Scaling Functions......Page 383
7.2.3 Wavelet Functions......Page 387
7.3.1 The Wavelet Series Expansions......Page 390
7.3.2 The Discrete Wavelet Transform......Page 392
7.3.3 The Continuous Wavelet Transform......Page 394
7.4 The Fast Wavelet Transform......Page 397
7.5 Wavelet Transforms in Two Dimensions......Page 404
7.6 Wavelet Packets......Page 412
References and Further Reading......Page 420
Problems......Page 422
8 - Image Compression......Page 427
8.1 Fundamentals......Page 429
8.1.1 Coding Redundancy......Page 430
8.1.2 Interpixel Redundancy......Page 432
8.1.3 Psychovisual Redundancy......Page 435
8.1.4 Fidelity Criteria......Page 437
8.2.1 The Source Encoder and Decoder......Page 439
8.2.2 The Channel Encoder and Decoder......Page 441
8.3.1 Measuring Information......Page 442
8.3.2 The Information Channel......Page 443
8.3.3 Fundamental Coding Theorems......Page 448
8.3.4 Using Information Theory......Page 455
8.4.1 Variable-Length Coding......Page 458
8.4.2 LZW Coding......Page 464
8.4.3 Bit-Plane Coding......Page 466
8.4.4 Lossless Predictive Coding......Page 474
8.5.1 Lossy Predictive Coding......Page 477
8.5.2 Transform Coding......Page 485
8.5.3 Wavelet Coding......Page 504
8.6 Image Compression Standards......Page 510
8.6.1 Binary Image Compression Standards......Page 511
8.6.2 Continuous Tone Still Image Compression Standards......Page 516
8.6.3 Video Compression Standards......Page 528
References and Further Reading......Page 531
Problems......Page 532
9 - Morphological lmage Processing......Page 537
9.1.1 Some Basic Concepts from Set Theory......Page 538
9.1.2 Logic Operations Involving Binary Images......Page 540
9.2.1 Dilation......Page 541
9.2.2 Erosion......Page 543
9.3 Opening and Closing......Page 546
9.4 The Hit-or-Miss Transformation......Page 550
9.5.1 Boundary Extraction......Page 552
9.5.2 Region Filling......Page 553
9.5.3 Extraction of Connected Components......Page 554
9.5.4 Convex Hull......Page 557
9.5.6 Thickening......Page 559
9.5.7 Skeletons......Page 561
9.5.8 Pruning......Page 563
9.5.9 Summary of Morphological Operations on Binary Images......Page 565
9.6.1 Dilation......Page 568
9.6.2 Erosion......Page 570
9.6.3 Opening and Closing......Page 572
9.6.4 Some Applications of Gray-Scale Morphology......Page 574
Problems......Page 578
10 - Image Segmentation......Page 585
10.1 Detection of Discontinuities......Page 586
10.1.1 Point Detection......Page 587
10.1.2 Line Detection......Page 588
10.1.3 Edge Detection......Page 590
10.2.1 Local Processing......Page 603
10.2.2 Global Processing via the Hough Transform......Page 605
10.2.3 Global Processing via Graph-Theoretic Techniques......Page 609
10.3.1 Foundation......Page 613
10.3.2 The Role of Illumination......Page 614
10.3.3 Basic Global Thresholding......Page 616
10.3.4 Basic Adaptive Thresholding......Page 618
10.3.5 Optimal Global and Adaptive Thresholding......Page 620
10.3.6 Use of Boundary Characteristics for Histogram Improvement and Local Thresholding......Page 626
10.3.7 Thresholds Based on Several Variables......Page 629
10.4.1 Basic Formulation......Page 630
10.4.2 Region Growing......Page 631
10.4.3 Region Splitting and Merging......Page 633
10.5.1 Basic Concepts......Page 635
10.5.2 Dam Construction......Page 638
10.5.3 Watershed Segmentation Algorithm......Page 640
10.5.4 The Use of Markers......Page 642
10.6.1 Spatial Techniques......Page 644
10.6.2 Frequency Domain Techniques......Page 648
References and Further Reading......Page 652
Problems......Page 654
11 - Representation and Description......Page 661
11.1.1 Chain Codes......Page 662
11.1.2 Polygonal Approximations......Page 664
11.1.3 Signatures......Page 666
11.1.4 Boundary Segments......Page 667
11.1.5 Skeletons......Page 668
11.2.1 Some Simple Descriptors......Page 671
11.2.2 Shape Numbers......Page 672
11.2.3 Fourier Descriptors......Page 673
11.2.4 Statistical Moments......Page 677
11.3 Regional Descriptors......Page 678
11.3.2 Topological Descriptors......Page 679
11.3.3 Texture......Page 683
11.3.4 Moments of Two-Dimensional Functions......Page 690
11.4 Use of Principal Components for Description......Page 693
11.5 Relational Descriptors......Page 701
References and Further Reading......Page 705
Problems......Page 707
12.1 Patterns and Pattern Classes......Page 711
12.2.1 Matching......Page 716
12.2.2 Optimum Statistical Classifiers......Page 722
12.2.3 Neural Networks......Page 730
12.3.1 Matching Shape Numbers......Page 750
12.3.2 String Matching......Page 752
12.3.3 Syntactic Recognition of Strings......Page 753
12.3.4 Syntactic Recognition of Trees......Page 758
Problems......Page 768
Bibliography......Page 773
Index......Page 797
Back Cover......Page 812