Description
Book SynopsisRafael C. Gonzalez received the B.S.E.E. degree from the University of Miami in 1965 and the M.E. and Ph.D. degrees in electrical engineering from the University of Florida, Gainesville, in 1967 and 1970, respectively. He joined the Electrical and Computer Engineering Department at University of Tennessee, Knoxville (UTK) in 1970, where he became Associate Professor in 1973, Professor in 1978, and Distinguished Service Professor in 1984. He served as Chairman of the department from 1994 through 1997. He is currently a Professor Emeritus at UTK.
Gonzalez is the founder of the Image & Pattern Analysis Laboratory and the Robotics & Computer Vision Laboratory at the University of Tennessee. He also founded Perceptics Corporation in 1982 and was its president until 1992. The last three years of this period were spent under a full-time employment contract with Westinghouse Corporation, who acquired the compa
Table of Contents
1. Introduction
What Is Digital Image Processing?
The Origins of Digital Image Processing
Examples of Fields that Use Digital Image Processing
Fundamental Steps in Digital Image Processing
Components of an Image Processing System
2. Digital Image Fundamentals
Elements of Visual Perception
Light and the Electromagnetic Spectrum. Image Sensing and Acquisition
Image Sampling and Quantization
Some Basic Relationships Between Pixels
An Introduction to the Mathematical Tools Used in Digital Image Processing
3. Intensity Transformations and Spatial Filtering
Background
Some Basic Intensity Transformation Functions
Histogram Processing. Fundamentals of Spatial Filtering
Smoothing Spatial Filters
Sharpening Spatial Filters
Combining Spatial Enhancement Methods
Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering
4. Filtering in the Frequency Domain
Background
Preliminary Concepts
Sampling and the Fourier Transform of Sampled Functions
The Discrete Fourier Transform (DFT) of One Variable
Extension to Functions of Two Variables
Some Properties of the 2-D Discrete Fourier Transform
The Basics of Filtering in the Frequency Domain
Image Smoothing Using Frequency Domain Filters
Image Sharpening Using Frequency Domain Filters
Selective Filtering
Implementation
5. Image Restoration and Reconstruction
A Model of the Image Degradation/Restoration Process
Noise Models
Restoration in the Presence of Noise Only–Spatial Filtering
Periodic Noise Reduction by Frequency Domain Filtering
Linear, Position-Invariant Degradations. Estimating the Degradation Function
Inverse Filtering
Minimum Mean Square Error (Wiener) Filtering
Constrained Least Squares Filtering. Geometric Mean Filter
Image Reconstruction from Projections.
6. Color Image Processing
Color Fundamentals
Color Models
Pseudocolor Image Processing
Basics of Full-Color Image Processing
Color Transformations. Smoothing and Sharpening
Image Segmentation Based on Color
Noise in Color Images
Color Image Compression
7. Wavelets and Multiresolution Processing
Background
Multiresolution Expansions
Wavelet Transforms in One Dimension
The Fast Wavelet Transform
Wavelet Transforms in Two
