本书是数字图像处理经典著作,作者在对32个**的134个院校和研究所的教师、学生及自学者进行广泛调查的基础上编写了第三版。除保留了第二版的大部分主要内容外,还根据收集的建议从13个方面进行了修订,新增400多幅图像、200多个图表和80多道习题,同时融入了近年来本科学领域的重要发展,使本书具有相当的特色与先进性。全书分为12章,包括绪论、数字图像基础、灰度变换与空间滤波、频域滤波、图像复原与重建、彩色图像处理、小波及多分辨率处理、图像压缩、形态学图像处理、图像分割、表现与描述、目标识别。

Preface
Acknowledgments
The Book Web Site
About the Authors
1 Introduction
1.1 What Is Digital Image Processing?
1.2 The Origins of Digital Image Processing
1.3 Examples of Fields that Use Digital Image Processing
1.4 Fundamental Steps in Digital Image Processing
1.5 Components of an Image Processing System
Summary
References and Further Reading
2 Digital Image Fundamentals
2.1 Elements of Visual Perception
2.2 Light and the Electromagnetic Spectrum
2.3 Image Sensing and Acquisition
2.4 Image Sampling and Quantization
2.5 Some Basic Relationships between Pixels
2.6 An Introduction to the Mathematical Tools Used in Digital Image Processing
Summary
2.5 Some Basic Relationships between Pixels
2.5.1 Neighbors of a Pixel
2.5.2 Adjacency, Connectivity, Regions, and Boundaries
2.5.3 Distance Measures
2.6 An Introduction to the Mathematical Tools Used in Digital Image Processing
2.6.1 Array versus Matrix Operations
2.6.2 Linear versus Nonlinear Operations
2.6.3 Arithmetic Operations
2.6.4 Set and Logical Operations
2.6.5 Spatial Operations
2.6.6 Vector and Matrix Operations
2.6.7 Image Transforms
2.6.8 Probabilistic Methods
Summary
References and Further Reading
Problems
3 Intensity Transformations and Spatial Filtering
3.1 Background
3.1.1 The Basics of Intensity Transformations and Spatial Filtering
3.1.2 About the Examples in This Chapter
3.2 Some Basic Intensity Transformation Functions
3.2.1 Image Negatives
3.2.2 Log Transformations
3.2.3 Power-Law (Gamma) Transformations
3.2.4 Piecewise-Linear Transformation Functions
3.3 Histogram Processing
3.3.1 Histogram Equalization
3.3.2 Histogram Matching (Specification)
3.3.3 Local Histogram Processing
3.3.4 Using Histogram Statistics for Image Enhancement
3.4 Fundamentals of Spatial Filtering
3.4.1 The Mechanics of Spatial Filtering
3.4.2 Spatial Correlation and Convolution
3.4.3 Vector Representation of Linear Filtering
3.4.4 Generating Spatial Filter Masks
3.5 Smoothing Spatial Filters
3.5.1 Smoothing Linear Filters
3.5.2 Order-Statistic (Nonlinear) Filters
3.6 Sharpening Spatial Filters
3.6.1 Foundation
3.6.2 Using the Second Derivative for Image Sharpening--The Laplacian
……