Digital Image Processing 2nded Gonzalez

Companion Website:\nDigital Image Processing, 2/E\nwww.prenhall.com/gonzalezwoods\n\nDigital Image Processing, 2/E is a completely self-contained book. The companion web site offers useful support in a number of important areas.\n\nFor the Student or Independent Reader the site contains:\n• Brief tutorials on probability, statistics, vectors, and matrices.\n• Complete solutions to selected problems.\n• A database containing images from the book and other educational sources.\n\nFor the Instructor the site contains:\n• Suggested curricula and sample laboratory projects.\n• Material removed from the previous edition, downloadable in convenient PDF format.\n• Presentation materials for the classroom.\n• Instructor's Manual containing complete solutions to all the problems in the book and solutions to sample laboratory projects. (Available only to instructors who have adopted the book for classroom use.)\n\nFor the Practitioner the book web site contains:\n• Links to sites that deal with various complementary aspects of image processing.\n• Listing of selected recent publications.\n• Bulletin board with announcements of conferences and other professional events in the field of image processing.\n• Listing of public domain and commercial image databases.\n\nThe web site provides the means to refresh material between editions by including new topics, digital images, recent developments, and information on emerging technology. Reference to the book's web site is designated in the margins of the book by use of the icon that appears below. Digital Image\nProcessing Digital Image\nProcessing\nSecond Edition\n\nRafael C. Gonzalez\nUniversity of Tennessee\nRichard E. Woods\nMedData Interactive\n\nPrentice Hall\nUpper Saddle River, New Jersey 07458 Image Enhancement in the Spatial Domain 75\n3.1 Background 76\n3.2 Some Basic Gray Level Transformations 78\n3.2.1 Image Negatives 78\n3.2.2 Log Transformations 79\n3.2.3 Power-Law Transformations 80\n3.2.4 Piecewise-Linear Transformation Functions 85\n3.3 Histogram Processing 88\n3.3.1 Histogram Equalization 91\n3.3.2 Histogram Matching (Specification) 94\n3.3.3 Local Enhancement 103\n3.3.4 Use of Histogram Statistics for Image Enhancement 103\n3.4 Enhancement Using Arithmetic/Logic Operations 108\n3.4.1 Image Subtraction 110\n3.4.2 Image Averaging 112\n3.5 Basics of Spatial Filtering 116\n3.6 Smoothing Spatial Filters 119\n3.6.1 Smoothing Linear Filters 119\n3.6.2 Order-Statistics Filters 123\n3.7 Sharpening Spatial Filters 125\n3.7.1 Foundation 125\n3.7.2 Use of Second Derivatives for Enhancement–The Laplacian 128\n3.7.3 Use of First Derivatives for Enhancement–The Gradient 134\n3.8 Combining Spatial Enhancement Methods 137\nSummary 141\nReferences and Further Reading 142\nProblems 142 To Connie, Ralph, and Robert\nand\nTo Janice, David, and Jonathan Image Enhancement in the Frequency Domain 147\n4.1 Background 148 Introduction to the Fourier Transform and the Frequency Domain 149\n6.1 The One-Dimensional Fourier Transform and its Inverse 150\n6.2 The Two-Dimensional DFT and Its Inverse 154\n6.3 Filtering in the Frequency Domain 156\n6.4 Correspondence between Filtering in the Spatial and Frequency Domains 161\n6.5 Smoothing Frequency-Domain Filters 167\n6.5.1 Ideal Lowpass Filters 167\n6.5.2 Butterworth Lowpass Filters 173\n6.5.3 Gaussian Lowpass Filters 175\n6.5.4 Additional Examples of Lowpass Filtering 178\n6.6 Sharpening Frequency Domain Filters 180\n6.6.1 Ideal Highpass Filters 182\n6.6.2 Butterworth Highpass Filters 183\n6.6.3 Gaussian Highpass Filters 184\n6.6.4 The Laplacian in the Frequency Domain 185\n6.6.5 Unsharp Masking, High-Boost Filtering, and High-Frequency Emphasis Filtering 187\n6.7 Implementation 194\n6.7.1 Some Additional Properties of the 2-D Fourier Transform 194\n6.7.2 Computing the Inverse Fourier Transform Using a Forward Transform Algorithm 198\n6.7.3 More on Periodicity: the Need for Padding 199\n6.7.4 The Convolution and Correlation Theorems 205\n6.7.5 Summary of Properties of the 2-D Fourier Transform 208\n6.7.6 The Fast Fourier Transform 208\n6.7.7 Some Comments on Filter Design 213\nSummary 214\nReferences 214\nProblems 215 Image Restoration 220\n7.1 A Model of the Image Degradation/Restoration Process 221\n7.2 Noise Models 222\n7.2.1 Spatial and Frequency Properties of Noise 222\n7.2.2 Some Important Noise Probability Density Functions 222\n7.2.3 Periodic Noise 227\n7.2.4 Estimation of Noise Parameters 227\n7.3 Restoration in the Presence of Noise Only–Spatial Filtering 230\n7.3.1 Mean Filters 231\n7.3.2 Order-Statistics Filters 233\n7.3.3 Adaptive Filters 237\n7.4 Periodic Noise Reduction by Frequency Domain Filtering 243\n7.4.1 Bandreject Filters 244\n7.4.2 Bandpass Filters 245\n7.4.3 Notch Filters 246\n7.4.4 Optimum Notch Filtering 248\n7.5 Linear, Position-Invariant Degradations 254\n7.6 Estimating the Degradation Function 256\n7.6.1 Estimation by Image Observation 256\n7.6.2 Estimation by Experimentation 257\n7.6.3 Estimation by Modeling 258\n7.7 Inverse Filtering 261\n7.8 Minimum Mean Square Error (Wiener) Filtering 262\n7.9 Constrained Least Squares Filtering 266\n7.10 Geometric Mean Filter 270\n7.11 Geometric Transformations 270\n7.11.1 Spatial Transformations 271\n7.11.2 Gray-Level Interpolation 272\nSummary 276\nReferences and Further Reading 277\nProblems 278 Color Fundamentals 283\n8.1 Color Models 289\n8.1.1 The RGB Color Model 290\n8.1.2 The CMY and CMYK Color Models 294\n8.1.3 The HSI Color Model 295\n8.2 Pseudocolor Image Processing 302\n8.2.1 Intensity Slicing 303\n8.2.2 Gray Level to Color Transformations 308\n8.3 Basics of Full-Color Image Processing 313\n8.4 Color Transformations 315\n8.4.1 Normalization 315\n8.4.2 Color Complements 318\n8.4.3 Color Slicing 320\n8.4.4 Tone and Color Corrections 322\n8.5 Histogram Processing 326\n8.6 Smoothing and Sharpening 327\n8.6.1 Color Image Smoothing 328\n8.6.2 Color Image Sharpening 330\n8.7 Color Segmentation 331\n8.7.1 Segmentation in HSI Color Space 331\n8.7.2 Segmentation in RGB Vector Space 333\n8.7.3 Color Edge Detection 335\n8.8 Noise in Color Images 339\n8.9 Color Image Compression 342\nSummary 343\nReferences and Further Reading 344\nProblems 344 Morphological Image Processing 519\n9.1 Preliminaries 520\n9.1.1 Some Basic Concepts from Set Theory 520\n9.1.2 Logic Operations Involving Binary Images 522\n9.2 Dilation and Erosion 523\n9.2.1 Dilation 523\n9.2.2 Erosion 525\n9.3 Opening and Closing 528\n9.4 The Hit-or-Miss Transformation 532\n9.5 Some Basic Morphological Algorithms 534\n9.5.1 Boundary Extraction 534\n9.5.2 Region Filling 535\n9.5.3 Extraction of Connected Components 536\n9.5.4 Convex Hull 539\n9.5.5 Thinning 541\n9.5.6 Thickening 541\n9.5.7 Skeletons 543\n9.5.8 Pruning 545\n9.5.9 Summary of Morphological Operations on Binary Images 547\n9.6 Extensions to Gray-Scale Images 550\n9.6.1 Dilation 550\n9.6.2 Erosion 552\n9.6.3 Opening and Closing 554\n9.6.4 Some Applications of Gray-Scale Morphology 556\nSummary 560\nReferences and Further Reading 560\nProblems 560