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冈萨雷斯编著的第三版英文版《数字图像处理》，第三版中文版《数字图像处理》的翻译源版。由于中文版中有许多翻译错误，所以非常建议，在学习的过程当中，多对照原英文版！此文档非扫描版本，内容图片非常清晰，带目录！Library of Congress Cataloging-in-Publication Data on FileVice president and editorial director Ecs Marcia. hortonExecutive editor: Michael Mcdonaldassociate editor: Alice dworkinEditorial Assistant: William opaluchManaging Editor: Scott DisannoProduction editor: rose kernanDirector of Creative Services: Paul BelfantiCreative Director: Juan Lopezart Director: Heather scottArt Editors: Gregory Dulles and Thomas BenfattiManufacturing Manager: Alexis Heydt-LongManufacturing Buyer: Lisa McDowellSenior Marketing Manager: Tim galligano 2008 by Pearson Education, IncPEARSON Pearson Prentice HallniceHallPearson education IncUpper saddle river, New Jersey 07458All rights reserved. No part of this book may be reproduced in any form or by any means, withoutpermission in writing from the publisherPearson prentice hall is a trademark of pearson education incThe authors and publisher of this book have used their best efforts in preparing this book. These efforts includethe development, research, and testing of the theories and programs to determine their effectiveness. The authorsand publisher make no warranty of any kind expressed or implied with regard to these programs or thedocumentation contained in this book The authors and publisher shall not be liable in any event for incidental orconsequential damages with, or arising out of, the furnishing, performance, or use of these programs.Printed in the united states of america10987654321工SBN口-13-1B吕7己吕-日7凸-0-1彐-1g凸7己-吕Pearson education ltd. LondonPearson Education Australia Pty. Ltd. SydneyPearson Education Singapore Pte, LtdPearson Education North Asia Ltd, Hong KongPearson Education Canada Inc. TorontoPearson educacion de mexico. S.A. de cvPearson Education-Japan, TokyoPearson Education malaysia, Pte LtdPearson Education, Inc, Upper Saddle river, New JerseyTo SamanthaaniTo Janice, david, and jonathanThis page intentionally left blankContentsPreface xuAcknowledgments xixThe book web site xxAbout the authors xxiⅠ ntroduction11.1 What Is Digital Image Processing? 11.2 The Origins of Digital Image Processing 31. 3 Examples of Fields that Use Digital Image Processing 71.3.1 Gamma-Ray Imaging 81.3.2 X-Ray Imaging 91.3.3 Imaging in the Ultraviolet Band 111.3.4 Imaging in the visible and Infrared Bands 121.3.5 Imaging in the Microwave Band 181.3.6 Imaging in the radio band 201.3.7 Examples in which Other Imaging Modalities Are Used 201.4 Fundamental Steps in Digital Image Processing 251.5 Components of an Image Processing System 28Summary 31References and Further Reading 31Digital image Fundamentals 352.1 Elements of Visual Perception 362.1.1 Structure of the human eye 362.1.2 Image Formation in the eye 382.1.3 Brightness Adaptation and discrimination 392.2 Light and the Electromagnetic Spectrum On 392.3 Image Sensing and Acquisition 462.3.1 Image Acquisition Using a Single Sensor 482.3.2 Image Acquisition Using Sensor Strips 482.3.3 Image Acquisition Using Sensor Arrays 502.3.4 A Simple image Formation Model 502.4 Image Sampling and Quantization 522.4.1 Basic Concepts in Sampling and Quantization 522.4.2 Representing Digital Images 552.4.3 Spatial and Intensity Resolution 592.4.4 Image Interpolation 65VI■ Content2.5 Some Basic relationships between Pixels 682.5. 1 Neighbors of a pixel 682.5.2 Adjacency, connectivity regions, and boundaries 682.5.3 Distance measures 712.6 An Introduction to the Mathematical Tools Used in Digital ImageProcessing2.6.1 Array versus Matrix Operations 722.6.2 Linear versus Nonlinear Operations 732.6.3 Arithmetic Operations 742.6.4 Set and Logical Operations 802.6.5 Spatial Operations 852.6.6 Vector and Matrix Operations 922.6.7 Image Transforms 932.6. 8 Probabilistic Methods 96Summary 98References and Further Reading 98Problems 99Intensity Transformations andSpatial Filtering 1043.1 Backsgd1053.1.1 The Basics of Intensity Transformations and Spatial Filtering 1053.1.2 About the Examples in This Chapter 1073.2 Some Basic Intensity Transformation Functions 1073.2.1 Image n1083. 2.2 Log Transformations 1093.2.3 Power-Law(Gamma) Transformations 1103.2.4 Piecewise-Linear Transformation Functions 1153.3 Histogram Processing 1203.3.1 Histogram Equalization 1223.3.3 Local Histogram Processing 139 e3.3.2 Histogram Matching(Specification3.3.4 Using histogram Statistics for Image Enhancement 1393.4 Fundamentals of Spatial Filtering 14g 6e3.4.1 The Mechanics of Spatial Filtering 1453.4.2 Spatial Correlation and Convolution 1463.4.3 Vector Representation of Linear Filtering 1503.4.4 Generating Spatial Filter Masks 1513.5 Smoothing Spatial Filters 1523.5.1 Smoothing linear filters 1523.5.2 Order-Statistic(Nonlinear) Filters 1563.6 Sharpening Spatial Filters 1573.6.1 Foundation 1583.6.2 USing the Second Derivative for Image Sharpening-TheLaplacian 160Contents3.6.3 Unsharp Masking and Highboost Filtering 1623.6.4 USing First-Order Derivatives for(Nonlinear)ImageSharpening-The Gradient 1653.7 Combining Spatial Enhancement Methods 1693.8 USing Fuzzy Techniques for Intensity Transformations and SpatialFiltering 1733.8.1 Introduction 1733.8.2 Principles of Fuzzy set Theory 1743.8.3 Using Fuzzy Sets 1783.8.4 USing Fuzzy Sets for Intensity Transformations 1863.8.5 Using Fuzzy Sets for Spatial Filtering 189Summary 192References and Further Reading 192Problems 193Filtering in the Frequency domain 1994.1 Background 2004.1.1 A Brief History of the Fourier Series and Transform 2004.2 Preliminary Concepts 21 this Chapter 2014.1.2 About the Examples ir4.2.1 Complex numbers 2024.2.2 Fourier series 2034.2.3 Impulses and Their Sifting Property 2034.2.4 The Fourier transform of functions of one ContinuousVariable 2054.2.5 Convolution 2094.3 Sampling and the Fourier Transform of Sampled Functions 2114.3.1 Sampling 2114.3.2 The Fourier Transform of Sampled Functions 2124.3.3 The Sampling Theorem 2134.3. 4 Aliasing 2174.3.5 Function Reconstruction(Recovery) from Sampled Data 2194.4 The Discrete Fourier Transform dFt) of One Variable 2204.4. 1 Obtaining the dFt from the Continuous Transform of aSampled Function 2214.4.2 Relationship Between the Sampling and FrequencyIntervals 2234.5 Extension to Functions of Two variables 2254.5. 1 The 2-D Impulse and its Sifting property 2254.5.2 The 2-D Continuous fourier transform pair 2264.5.3 Two-Dimensional Sampling and the 2-D SamplingTheorem 2274.5.4 Aliasing in Images 2284.5.5 The 2-D Discrete Fourier Transform and Its Inverse 235Contents4.6 Some Properties of the 2-D Discrete Fourier Transform 2364.6.1 Relationships Between Spatial and Frequency Intervals 2364.6.2 Translation and rotation 2364.6.3 Periodicity 2374.6.4 Symmetry properties 2394.6.5 Fourier Spectrum and Phase angle 2454.6.6 The 2-D Convolution Theorem 2494.7 The Basics of Filtering in the Frequency Domain 25f4.6.7 Summary of 2-D discrete Fourier Transform Properties 2534.7.1 Additional Characteristics of the Frequency domain 2554.7.2 Frequency Domain Filtering Fundamentals 2574.7.3 Summary of Steps for Filtering in the Frequency Domain 2634.7.4 Correspondence Between Filtering in the Spatial and FrequencyDomains 2634.8 Image Smoothing Using Frequency Domain Filters 2694.8.1 Ideal Lowpass Filters 2694.8.2 Butterworth Lowpass Filters 2734.8.3 Gaussian Lowpass Filters 2764.8.4 Additional Examples of Lowpass Filtering 2774. 9 Image Sharpening Using Frequency Domain Filters 2804.9.1 Ideal Highpass Filters 2814.9.2 Butterworth Highpass Filters 2844.9.3 Gaussian Highpass Filters 2854.9.4 The laplacian in the frequency domain 2864.9.5 Unsharp Masking, Highboost Filtering, and High-FrequencyEmphasis Filtering 2884.9.6 Homomorphic FilteringnQO4.10 Selective Filtering 2944.10.1 Bandreject and Bandpass Filters 2944.10.2 Notch Filters 2944.11 Implementation 2984.11.1 Separability of the 2-D DFT 2984.11.2 Computing the IDFt Using a DFT Algorithm 2994. 11.3 The Fast Fourier Transform(FFT) 2994.11. 4 Some Comments on Filter Design 303Summary 303References and Further reading 304Problems 304Image restoration and Reconstruction 3115.1 A Model of the Image Degradation/Restoration Process 3125.2 Noise models 3135.2.1 Spatial and Frequency Properties of Noise 3135.2.2 Some Important noise probability density Functions 314■C5.2.3 Periodic noise 3185.2.4 Estimation of noise parameters 3195.3 Restoration in the Presence of Noise Only--Spatial Filtering 3225.3.1 Mean Filters 3225.3.2 Order-Statistic Filters 3255.3.3 Adaptive Filters 3305.4 Periodic Noise reduction by Frequency Domain Filtering 3355.4.1 Bandreject Filters 3355.4.2 Bandpass Filters 3365.4.3 Notch Filters 3375.4.4 Optimum Notch Filtering 3385.5 Linear, Position-Invariant Degradations 3435.6 Estimating the Degradation Function 3465.6.1 Estimation by Image Observation 3465.6.2 Estimation by Experimentation 3475.6.3 Estimation by Modeling 3475.7 Inverse Filtering 3515.9 Constrained Least Squares Filtering 357ring 3525.8 Minimum Mean Square Error (Wiener) Filte5.10 Geometric mean filter 3615.11 Image Reconstruction from Projections 3625.11.1 Introduction 3625.11.2 Principles of Computed Tomography(CT) 3655.11.3 Projections and the radon Transform 3685.11.4 The fourier -Slice Theorem 3745.11.5 Reconstruction Using Parallel-Beam Filtered Backprojections5.11.6 Reconstruction Using Fan-Beam Filtered Backprojections 381Summary 387References and Further reading 388Problems 389Color image processing 3946.1 Color fundamentals 3956.2 Color models 4016.2.1 The rgb color model 4026.2.2 The CMy and cmyk color models 4066.2.3 The hsi Color model 4076.3 Pseudocolor Image Processing 4146.3.1 Intensity Slicing 4156.3.2 Intensity to Color Transformations 4186.4 Basics of Full-Color Image Processing 4246.5 Color Transformations 4266.5.1 Formulation 4266.5.2 Color Complements 430
