Digital.Image.Processing.4th.Edition.Feb.2007:Digital.Image.Processing.4th.Edition.Feb.2007
About the cover:
The first image on the left is the peppers_gamma original color image.
The second image is the edge map of the luma component of the first image produced by a derivative of
Gaussian edge detector.
The third image is the cat original color image.
The fourth image is the spatial gain image of the luma component of the cat image produced by a Wallis
statistical differencing operator.
The fifth image is the result of Wallis processing on the luma component and amplitude stretching of the
chrominance components of the cat image.
The lower right image is a sharpened version of the original image obtained by subtracting an amplitude
weighted version of the blurred image from a weighted version of the original image. The processing
technique is called unsharp masking.
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Library of Congress Cataloging-in-Publication Data:
Pratt, William K.
Digital image processing : PIKS Scientific inside / William K. Pratt.—, 4th ed.
p. cm.
“A Wiley-Interscience publication.”
Includes bibliographical references and index.
ISBN: 978-0-471-76777-0
1. Image processing—Digital techniques. I. Title.
TA1632.P7 2007
621.36'7—dc22 2006046397
Printed in the United States of America.
10 9 8 7 6 5 4 3 2 1
Preface xiii
Acknowledgments xvii
PART 1 CONTINUOUS IMAGE CHARACTERIZATION 1
1 Continuous Image Mathematical Characterization 3
1.1 Image Representation, 3
1.2 Two-Dimensional Systems, 5
1.3 Two-Dimensional Fourier Transform, 10
1.4 Image Stochastic Characterization, 14
2 Psychophysical Vision Properties 23
2.1 Light Perception, 23
2.2 Eye Physiology, 26
2.3 Visual Phenomena, 29
2.4 Monochrome Vision Model, 33
2.5 Color Vision Model, 39
3 Photometry and Colorimetry 45
3.1 Photometry, 45
3.2 Color Matching, 49
viii CONTENTS
3.3 Colorimetry Concepts, 54
3.4 Tristimulus Value Transformation, 61
3.5 Color Spaces, 63
PART 2 DIGITAL IMAGE CHARACTERIZATION 89
4 Image Sampling and Reconstruction 91
4.1 Image Sampling and Reconstruction Concepts, 91
4.2 Monochrome Image Sampling Systems, 99
4.3 Monochrome Image Reconstruction Systems, 110
4.4 Color Image Sampling Systems, 119
5 Image Quantization 127
5.1 Scalar Quantization, 127
5.2 Processing Quantized Variables, 133
5.3 Monochrome and Color Image Quantization, 136
PART 3 DISCRETE TWO-DIMENSIONAL PROCESSING 145
6 Discrete Image Mathematical Characterization 147
6.1 Vector-Space Image Representation, 147
6.2 Generalized Two-Dimensional Linear Operator, 149
6.3 Image Statistical Characterization, 153
6.4 Image Probability Density Models, 158
6.5 Linear Operator Statistical Representation, 162
7 Superposition and Convolution 165
7.1 Finite-Area Superposition and Convolution, 165
7.2 Sampled Image Superposition and Convolution, 174
7.3 Circulant Superposition and Convolution, 181
7.4 Superposition and Convolution Operator Relationships, 184
8 Unitary Transforms 189
8.1 General Unitary Transforms, 189
8.2 Fourier Transform, 193
8.3 Cosine, Sine and Hartley Transforms, 199
8.4 Hadamard, Haar and Daubechies Transforms, 204
8.5 Karhunen–Loeve Transform, 211
CONTENTS ix
9 Linear Processing Techniques 217
9.1 Transform Domain Processing, 217
9.2 Transform Domain Superposition, 220
9.3 Fast Fourier Transform Convolution, 225
9.4 Fourier Transform Filtering, 233
9.5 Small Generating Kernel Convolution, 241
PART 4 IMAGE IMPROVEMENT 245
10 Image Enhancement 247
10.1 Contrast Manipulation, 248
10.2 Histogram Modification, 259
10.3 Noise Cleaning, 267
10.4 Edge Crispening, 284
10.5 Color Image Enhancement, 291
10.6 Multispectral Image Enhancement, 298
11 Image Restoration Models 307
11.1 General Image Restoration Models, 307
11.2 Optical Systems Models, 310
11.3 Photographic Process Models, 314
11.4 Discrete Image Restoration Models, 322
12 Image Restoration Techniques 329
12.1 Sensor and Display Point Nonlinearity Correction, 329
12.2 Continuous Image Spatial Filtering Restoration, 335
12.3 Pseudoinverse Spatial Image Restoration, 345
12.4 SVD Pseudoinverse Spatial Image Restoration, 359
12.5 Statistical Estimation Spatial Image Restoration, 364
12.6 Constrained Image Restoration, 369
12.7 Blind Image Restoration, 373
12.8 Multi-Plane Image Restoration, 379
13 Geometrical Image Modification 387
13.1 Basic Geometrical Methods, 387
13.2 Spatial Warping, 400
13.3 Perspective Transformation, 404
13.4 Camera Imaging Model, 407
13.5 Geometrical Image Resampling, 410
x CONTENTS
PART 5 IMAGE ANALYSIS 419
14 Morphological Image Processing 421
14.1 Binary Image Connectivity, 421
14.2 Binary Image Hit or Miss Transformations, 424
14.3 Binary Image Shrinking, Thinning, Skeletonizing and Thickening, 431
14.4 Binary Image Generalized Dilation and Erosion, 442
14.5 Binary Image Close and Open Operations, 453
14.6 Gray Scale Image Morphological Operations, 455
15 Edge Detection 465
15.1 Edge, Line and Spot Models, 465
15.2 First-Order Derivative Edge Detection, 471
15.3 Second-Order Derivative Edge Detection, 492
15.4 Edge-Fitting Edge Detection, 506
15.5 Luminance Edge Detector Performance, 508
15.6 Color Edge Detection, 522
15.7 Line and Spot Detection, 529
16 Image Feature Extraction 535
16.1 Image Feature Evaluation, 535
16.2 Amplitude Features, 537
16.3 Transform Coefficient Features, 542
16.4 Texture Definition, 545
16.5 Visual Texture Discrimination, 547
16.6 Texture Features, 555
17 Image Segmentation 579
17.1 Amplitude Segmentation, 580
17.2 Clustering Segmentation, 587
17.3 Region Segmentation, 590
17.4 Boundary Segmentation, 595
17.5 Texture Segmentation, 611
17.6 Segment Labeling, 613
18 Shape Analysis 623
18.1 Topological Attributes, 623
18.2 Distance, Perimeter and Area Measurements, 625
18.3 Spatial Moments, 631
18.4 Shape Orientation Descriptors, 643
18.5 Fourier Descriptors, 645
18.6 Thinning and Skeletonizing, 647
CONTENTS xi
19 Image Detection and Registration 651
19.1 Template Matching, 651
19.2 Matched Filtering of Continuous Images, 655
19.3 Matched Filtering of Discrete Images, 662
19.4 Image Registration, 664
PART 6 IMAGE PROCESSING SOFTWARE 679
20 PIKS Image Processing Software 681
20.1 PIKS Functional Overview, 681
20.2 PIKS Scientific Overview, 704
21 PIKS Image Processing Programming Exercises 715
21.1 Program Generation Exercises, 716
21.2 Image Manipulation Exercises, 717
21.3 Color Space Exercises, 718
21.4 Region-of-Interest Exercises, 720
21.5 Image Measurement Exercises, 721
21.6 Quantization Exercises, 722
21.7 Convolution Exercises, 723
21.8 Unitary Transform Exercises, 724
21.9 Linear Processing Exercises, 725
21.10 Image Enhancement Exercises, 726
21.11 Image Restoration Models Exercises, 728
21.12 Image Restoration Exercises, 729
21.13 Geometrical Image Modification Exercises, 729
21.14 Morphological Image Processing Exercises, 730
21.15 Edge Detection Exercises, 732
21.16 Image Feature Extraction Exercises, 733
21.17 Image Segmentation Exercises, 734
21.18 Shape Analysis Exercises, 735
21.19 Image Detection and Registration Exercises, 735
Appendix 1 Vector-Space Algebra Concepts 737
Appendix 2 Color Coordinate Conversion 753
Appendix 3 Image Error Measures 759
Appendix 4 PIKS Compact Disk 761
Bibliography 763
Index 769
PREFACE
In January 1978, I began the preface to the first edition of Digital Image Processing
with the following statement:
“The field of image processing has grown considerably during the past
decade with the increased utilization of imagery in myriad applications
coupled with improvements in the size, speed and cost effectiveness of digital
computers and related signal processing technologies. Image processing
has found a significant role in scientific, industrial, space and government
applications.”
In January 1991, in the preface to the second edition, I stated:
“Thirteen years later as I write this preface to the second edition, I find
the quoted statement still to be valid. The 1980s have been a decade of significant
growth and maturity in this field. At the beginning of that decade,
many image processing techniques were of academic interest only; their
execution was too slow and too costly. Today, thanks to algorithmic and
implementation advances, image processing has become a vital cost-effective
technology in a host of applications.”
In August 2000, in the preface to the third edition, I wrote:
“Now, in this beginning of the twenty-first century, image processing
has become a mature engineering discipline. But advances in the theoretical
basis of image processing continue. Some of the reasons for this third
edition of the book are to correct defects in the second edition, delete content
of marginal interest, and add discussion of new, important topics.
Another motivating factor is the inclusion of interactive, computer display
xiv PREFACE
imaging examples to illustrate image processing concepts. Finally, this
third edition includes computer programming exercises to bolster its theoretical
content. These exercises can be implemented using the Programmer’s
Imaging Kernel System (PIKS) application program interface (API).
PIKS is an International Standards Organization (ISO) standard library of
image processing operators and associated utilities.”
Again, for a fourth time, a new edition of Digital Image Processing is offered to
the image processing community. Why? One reason is because advances in the theoretical
aspects of image processing technology continue at a rapid rate. For example,
in the year 2005, the IEEE Transactions on Image Processing published 2191
pages of research papers. The IEEE Transactions on Pattern Analysis and Machine
Intelligence was in close second place in 2005 with 2002 published pages. Add to
that the content of independent journals, such as the John Wiley & Sons International
Journal of Imaging Systems and Technology plus numerous image processing
technical conferences. There is an enormous amount of new image processing technology
to be absorbed. I have tried to act as a publishing filter by culling through the
image processing literature since the third edition was published in 2002, and then
abstracting what I think are the most important contributions. Details follow.
Another reason for publication of the fourth edition of Digital Image Processing
is to make available, at no cost, the PIKS Scientific API for educational purposes
and for industrial software development. The PIKS Scientific software is on a CD
affixed to the back cover of this book. PIKS Scientific includes implementations of
most of the high-level operators in this book.
The book is intended to be an “industrial strength” introduction to digital image
processing to be used as a text for an electrical engineering or computer science
course on the subject. Also, it can be used as a reference manual for scientists who
are engaged in image processing research, developers of image processing hardware
and software systems, and practicing engineers and scientists who use image processing
as a tool in their applications. Mathematical derivations are provided for
most algorithms. The reader is assumed to have a basic background in linear system
theory, vector space algebra and random processes. Proficiency in C language programming
is necessary for execution of the image processing programming exercises
using PIKS.
The book is divided into six parts. The first three parts cover the basic technologies
that are needed to support image processing applications.
Part 1 contains three chapters concerned with the characterization of continuous
images. Topics include the mathematical representation of continuous images, the
psychophysical properties of human vision, and photometry and colorimetry. No
substantial changes have been made to this fundamental material.
In Part 2, image sampling and quantization techniques are explored along with
the mathematical representation of discrete images. A new section on Color Image
Sampling Systems, such as the Bayer color filter, has been added to Chapter 4.
Part 3 discusses two-dimensional signal processing techniques, including general
linear operators and unitary transforms such as the Fourier, Hadamard, Daubechies
and Karhunen–Loeve transforms. The final chapter in Part 3 analyzes and compares
PREFACE xv
linear processing techniques implemented by direct convolution and Fourier domain
filtering.
The next two parts of the book cover the two principal application areas of image
processing: Image Improvement and Image Analysis.
Part 4 presents a discussion of image enhancement and restoration techniques,
including restoration models, point and spatial restoration and geometrical image
modification. Chapter 10 on Image Enhancement contains new material on Contrast
Manipulation, Histogram Modification Noise Cleaning and Color Image Enhancement.
Content on Blind Image Restoration and Multi-Plane Image Restoration has
been added to Chapter 12, Image Restoration Techniques. A new section on Polar
Coordinate Conversion has been included in the chapter on Geometrical Image
Modification.
Part 5, entitled Image Analysis, concentrates on the extraction of information
from an image. Specific topics include morphological image processing, edge
detection, image feature extraction, image segmentation, object shape analysis and
object detection. Additional material on Structuring Element Decomposition has
been included in the Morphological Image Processing chapter. The sections on First
Order Derivative Edge Detection and Color Edge Detection in Chapter 15 have
been augmented. Material has been added on Texture Features in Chapter 16. In the
chapter on Image Segmentation, material has been added on Amplitude, Region,
Boundary and Texture Segmentation. New content on Distance, Perimeter and Area
Measurements has been added to the Shape Analysis chapter. A new section on
Non-morphological Thinning and Skeletonizing has been included in the chapter.
Finally, new material has been added on Template Matching and Image Registration
in Chapter 19.
Part 6 discusses the software implementation of image processing applications.
This part describes the PIKS API and explains its use as a means of implementing
image processing algorithms. Image processing programming exercises are included
in Part 6.
Throughout the first 19 chapters on the theoretical basis of image processing, upto-
date references of papers judged to be of significance have been included as a
guide for extended study.
Although readers should find this book reasonably comprehensive, many important
topics allied to the field of digital image processing have been omitted to limit
the size and cost of the book. Among the most prominent omissions are the topics of
pattern recognition, image reconstruction from projections, image understanding,
image coding, scientific visualization and computer graphics.
WILLIAM K. PRATT
Los Altos, California
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感谢楼主无私奉献
祝大家新年快乐！
太棒了
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感谢版主无私奉献！！:29bb
happy new year ,thank you for the job
新书必定！:))1 :))1 :))1
感谢楼主分享
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谢谢分享！
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好人，好书！
Digital.Image.Processing.4th.Edition.Feb.2007: Digital.Image.Processing.4th.Edition.Feb.2007.eBook-LinG.part2.rar
