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Digital Image Processing, 4th Edition

By Rafael C. Gonzalez, Richard E. Woods

Published by Pearson

Published Date: Mar 20, 2017


Introduce your students to image processing with the industry’s most prized text

For 40 years, Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals.

The 4th Edition, which celebrates the book’s 40th anniversary, is based on an extensive survey of faculty, students, and independent readers in 150 institutions from 30 countries. Their feedback led to expanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maximally-stable extremal regions (MSERs), graph cuts, k-means clustering and superpixels, active contours (snakes and level sets), and exact histogram matching.  Major improvements were made in reorganizing the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering.  Major revisions and additions were made to examples and homework exercises throughout the book. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for you and your teacher containing, solutions, image databases, and sample code.   

The support materials for this title can be found at 

Table of Contents

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

   Gamma-Ray Imaging

   X-Ray Imaging

   Imaging in the Ultraviolet Band

   Imaging in the Visible and Infrared Bands

   Imaging in the Microwave Band

   Imaging in the Radio Band

   Other Imaging Modalities

1.4 Fundamental Steps in Digital Image Processing

1.5 Components of an Image Processing System

2 Digital Image Fundamentals

2.1 Elements of Visual Perception

   Structure of the Human Eye

   Image Formation in the Eye

   Brightness Adaptation and Discrimination

2.2 Light and the Electromagnetic Spectrum

2.3 Image Sensing and Acquisition

   Image Acquisition Using a Single Sensing Element

   Image Acquisition Using Sensor Strips

   Image Acquisition Using Sensor Arrays

   A Simple Image Formation Model

2.4 Image Sampling and Quantization

   Basic Concepts in Sampling and Quantization

   Representing Digital Images

   Linear vs. Coordinate Indexing

   Spatial and Intensity Resolution

   Image Interpolation

2.5 Some Basic Relationships Between Pixels

   Neighbors of a Pixel

   Adjacency, Connectivity, Regions, and Boundaries

   Distance Measures

2.6 Introduction to the Basic Mathematical Tools Used in Digital Image Processing

   Elementwise versus Matrix Operations

   Linear versus Nonlinear Operations

   Arithmetic Operations

   Set and Logical Operations

            Basic Set Operations

            Logical Operations

            Fuzzy Sets

   Spatial Operations

            Single-Pixel Operations

            Neighborhood Operations

            Geometric Transformations

            Image Registration

   Vector and Matrix Operations

   Image Transforms

   Probability and Random Variables

3 Intensity Transformations and Spatial Filtering

3.1 Background

   The Basics of Intensity Transformations and Spatial Filtering

   About the Examples in this Chapter

3.2 Some Basic Intensity Transformation Functions

   Image Negatives

   Log Transformations

   Power-Law (Gamma) Transformations

   Piecewise Linear Transformation Functions

            Contrast Stretching

            Intensity-Level Slicing

            Bit-Plane Slicing

3.3 Histogram Processing

   Histogram Equalization

   Histogram Matching (Specification)

   Exact Histogram Matching (Specification)



                        Computing the neighborhood averages and extracting the K-tuples:

                  Exact Histogram Specification Algorithm

   Local Histogram Processing

   Using Histogram Statistics for Image Enhancement

3.4 Fundamentals of Spatial Filtering

   The Mechanics of Linear Spatial Filtering

   Spatial Correlation and Convolution

   Separable Filter Kernels

   Some Important Comparisons Between Filtering in the Spatial and Frequency Domains

   A Word about how Spatial Filter Kernels are Constructed

3.5 Smoothing (Lowpass) Spatial Filters

   Box Filter Kernels

   Lowpass Gaussian Filter Kernels

   Order-Statistic (Nonlinear) Filters

3.6 Sharpening (Highpass) Spatial Filters


   Using the Second Derivative for Image Sharpening—The Laplacian

   Unsharp Masking and Highboost Filtering

   Using First-Order Derivatives for Image Sharpening—The Gradient

3.7 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters

3.8 Combining Spatial Enhancement Methods

3.9 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering


   Principles of Fuzzy Set Theory


                  Some Common Membership Functions

   Using Fuzzy Sets

   Using Fuzzy Sets for Intensity Transformations

   Using Fuzzy Sets for Spatial Filtering

4 Filtering in the Frequency Domain

4.1 Background

   A Brief History of the Fourier Series and Transform

   About the Examples in this Chapter

4.2 Preliminary Concepts

   Complex Numbers

   Fourier Series

   Impulses and their Sifting Properties

   The Fourier Transform of Functions of One Continuous Variable


4.3 Sampling and the Fourier Transform of Sampled Functions


   The Fourier Transform of Sampled Functions

   The Sampling Theorem


   Function Reconstruction (Recovery) from Sampled Data

4.4 The Discrete Fourier Transform of One Variable

   Obtaining the DFT from the Continuous Transform of a Sampled Function

   Relationship Between the Sampling and Frequency Intervals

4.5 Extensions to Functions of Two Variables

   The 2-D Impulse and Its Sifting Property

   The 2-D Continuous Fourier Transform Pair

   2-D Sampling and the 2-D Sampling Theorem

   Aliasing in Images

                  Extensions from 1-D Aliasing

                  Image Resampling and Interpolation

                  Aliasing and Moiré Patterns

   The 2-D Discrete Fourier Transform and Its Inverse

4.6 Some Properties of the 2-D DFT and IDFT

   Relationships Between Spatial and Frequency Intervals

   Translation and Rotation


   Symmetry Properties

   Fourier Spectrum and Phase Angle

   The 2-D Discrete Convolution Theorem

   Summary of 2-D Discrete Fourier Transform Properties

4.7 The Basics of Filtering in the Frequency Domain

   Additional Characteristics of the Frequency Domain

   Frequency Domain Filtering Fundamentals

   Summary of Steps for Filtering in the Frequency Domain

   Correspondence Between Filtering in the Spatial and

   Frequency Domains

4.8 Image Smoothing Using Lowpass Frequency Domain Filters

   Ideal Lowpass Filters

   Gaussian Lowpass Filters

   Butterworth Lowpass Filters

   Additional Examples of Lowpass Filtering

4.9 Image Sharpening Using Highpass Filters

   Ideal, Gaussian, and Butterworth Highpass Filters from Lowpass Filters

   The Laplacian in the Frequency Domain

   Unsharp Masking, High-boost Filtering, and High-Frequency-Emphasis Filtering

   Homomorphic Filtering

4.10 Selective Filtering

   Bandreject and Bandpass Filters

   Notch Filters

4.11 The Fast Fourier Transform

   Separability of the 2-D DFT

   Computing the IDFT Using a DFT Algorithm

   The Fast Fourier Transform (FFT)

5 Image Restoration and Reconstruction

5.1 A Model of the Image Degradation/Restoration Process

5.2 Noise Models

   Spatial and Frequency Properties of Noise

   Some Important Noise Probability Density Functions

                  Gaussian Noise

                  Rayleigh Noise

                  Erlang (Gamma) Noise

                  Exponential Noise

                  Uniform Noise

                  Salt-and-Pepper Noise

   Periodic Noise

   Estimating Noise Parameters

5.3 Restoration in the Presence of Noise Only—Spatial Filtering

   Mean Filters

                  Arithmetic Mean Filter

                  Geometric Mean Filter

                  Harmonic Mean Filter

                  Contraharmonic Mean Filter

   Order-Statistic Filters

                  Median Filter

                  Max and Min Filters

                  Midpoint Filter

                  Alpha-Trimmed Mean Filter

   Adaptive Filters

                  Adaptive, Local Noise Reduction Filter

                  Adaptive Median Filter

5.4 Periodic Noise Reduction Using Frequency Domain Filtering

   More on Notch Filtering

   Optimum Notch Filtering

5.5 Linear, Position-Invariant Degradations

5.6 Estimating the Degradation Function

   Estimation by Image Observation

   Estimation by Experimentation

   Estimation by Modeling

5.7 Inverse Filtering

5.8 Minimum Mean Square Error (Wiener) Filtering

5.9 Constrained Least Squares Filtering

5.10 Geometric Mean Filter

5.11 Image Reconstruction from Projections


   Principles of X-ray Computed Tomography (CT)

   Projections and the Radon Transform


   The Fourier-Slice Theorem

   Reconstruction Using Parallel-Beam Filtered Backprojections

   Reconstruction Using Fan-Beam Filtered Backprojections

6 Wavelet and Other Image Transforms

6.1 Preliminaries

6.2 Matrix-based Transforms

   Rectangular Arrays

   Complex Orthonormal Basis Vectors

   Biorthonormal Basis Vectors

6.3 Correlation

6.4 Basis Functions in the Time-Frequency Plane

6.5 Basis Images

6.6 Fourier-Related Transforms

   The Discrete hartley Transform

   The Discrete Cosine Transform

   The Discrete Sine Transform

6.7 Walsh-Hadamard Transforms

6.8 Slant Transform

6.9 Haar Transform

6.10 Wavelet Transforms

   Scaling Functions

   Wavelet Functions

   Wavelet Series Expansion

   Discrete Wavelet Transform in One Dimension

                  The Fast Wavelet Transform

   Wavelet Transforms in Two Dimensions

   Wavelet Packets

7 Color Image Processing

7.1 Color Fundamentals

7.2 Color Models

   The RGB Color Model

   The CMY and CMYK Color Models

   The HSI Color Model

                  Converting Colors from RGB to HSI

                  Converting Colors from HSI to RGB

                  Manipulating HSI Component Images

   A Device Independent Color Model

7.3 Pseudocolor Image Processing

   Intensity Slicing and Color Coding

   Intensity to Color Transformations

7.4 Basics of Full-Color Image Processing

7.5 Color Transformations


   Color Complements

   Color Slicing

   Tone and Color Corrections

   Histogram Processing of Color Images

7.6 Color Image Smoothing and Sharpening

   Color Image Smoothing

   Color Image Sharpening

7.7 Using Color in Image Segmentation

   Segmentation in HSI Color Space

   Segmentation in RGB Space

   Color Edge Detection

7.8 Noise in Color Images

7.9 Color Image Compression

8 Image Compression and Watermarking

8.1 Fundamentals

   Coding Redundancy

   Spatial and Temporal Redundancy

   Irrelevant Information

   Measuring Image Information

                  Shannon’s First Theorem

   Fidelity Criteria

   Image Compression Models

                  The Encoding or Compression Process

                  The Decoding or Decompression Process

   Image Formats, Containers, and Compression Standards

8.2 Huffman Coding

8.3 Golomb Coding

8.4 Arithmetic Coding

   Adaptive context dependent probability estimates

8.5 LZW Coding

8.6 Run-length Coding

   One-dimensional CCITT compression

   Two-dimensional CCITT compression

8.7 Symbol-based Coding

   JBIG2 compression

8.8 Bit-plane Coding

8.9 Block Transform Coding

   Transform selection

   Subimage size selection

   Bit allocation

                  Zonal Coding Implementation

                  Threshold Coding Implementation


8.10 Predictive Coding

   Lossless predictive coding

   Motion compensated prediction residuals

   Lossy predictive coding

   Optimal predictors

   Optimal quantization

8.11 Wavelet Coding

   Wavlet selection

   Decomposition level selection

   Quantizer design


8.12 Digital Image Watermarking

9 Morphological Image Processing

9.1 Preliminaries

9.2 Erosion and Dilation




9.3 Opening and Closing

9.4 The Hit-or-Miss Transform

9.5 Some Basic Morphological Algorithms

   Boundary Extraction

   Hole Filling

   Extraction of Connected Components

   Convex Hull





9.6 Morphological Reconstruction

   Geodesic Dilation and Erosion

   Morphological Reconstruction by Dilation and by Erosion

   Sample Applications

                  Opening by Reconstruction

                  Automatic Algorithm for Filling Holes

                  Border Clearing

9.7 Summary of Morphological Operations on Binary Images

9.8 Grayscale Morphology

   Grayscale Erosion and Dilation

   Grayscale Opening and Closing

   Some Basic Grayscale Morphological Algorithms

                  Morphological Smoothing

                  Morphological Gradient

                  Top-Hat and Bottom-Hat Transformations


                  Textural Segmentation

   Grayscale Morphological Reconstruction

10 Image Segmentation I: Edge Detection,

   Thresholding, and Region Detection

10.1 Fundamentals

10.2 Point, Line, and Edge Detection


   Detection of Isolated Points

   Line Detection

   Edge Models

   Basic Edge Detection

                  The Image Gradient and Its Properties

                  Gradient Operators

                  Combining the Gradient with Thresholding

   More Advanced Techniques for Edge Detection

                  The Marr-Hildreth Edge Detector

                  The Canny Edge Detector

   Linking Edge Points

                  Local Processing

                  Global Processing Using the Hough Transform

10.3 Thresholding


                  The Basics of Intensity Thresholding

                  The Role of Noise in Image Thresholding

                  The Role of Illumination and Reflectance in Image Thresholding

   Basic Global Thresholding

   Optimum Global Thresholding Using Otsu’s Method

   Using Image Smoothing to Improve Global Thresholding

   Using Edges to Improve Global Thresholding

   Multiple Thresholds

   Variable Thresholding

                  Variable Thresholding Based on Local Image Properties

                  Variable Thresholding Based on Moving Averages

10.4 Segmentation by Region Growing and by Region Splitting and Merging

   Region Growing

   Region Splitting and Merging

10.5 Region Segmentation Using Clustering and Superpixels

   Region Segmentation using K-Means Clustering

   Region Segmentation using Superpixels

                  SLIC Superpixel Algorithm

                  Specifying the Distance Measure

10.6 Region Segmentation Using Graph Cuts

   Images as Graphs

   Minimum Graph Cuts

   Computing Minimal Graph Cuts

   Graph Cut Segmentation Algorithm

10.7 Segmentation Using Morphological Watersheds


   Dam Construction

   Watershed Segmentation Algorithm

   The Use of Markers

10.8 The Use of Motion in Segmentation

   Spatial Techniques

                  A Basic Approach

                  Accumulative Differences

                  Establishing a Reference Image

   Frequency Domain Techniques

11 Image Segmentation II: Active Contours: Snakes and Level Sets

11.1 Background

11.2 Image Segmentation Using Snakes

   Explicit (Parametric) Representation of Active Contours

   Derivation of the Fundamental Snake Equation

   Iterative Solution of the Snake Equation

   External Force Based on the Magnitude of the Image

   Gradient (MOG)

   External Force Based on Gradient Vector Flow (GVF)

11.3 Segmentation Using Level Sets

   Implicit Representation of Active Contours

   Derivation of the Level Set Equation

   Discrete (Iterative) Solution of The Level Set Equation


   Specifying, Initializing, and Reinitializing Level Set Functions

   Force Functions Based Only on Image Properties

   Edge/Curvature-Based Forces

   Region/Curvature-Based Forces

   Improving the Computational Performance of Level Set Algorithms

12 Feature Extraction

12.1 Background

12.2 Boundary Preprocessing

   Boundary Following (Tracing)

   Chain Codes

                  Freeman Chain Codes

                  Slope Chain Codes

   Boundary Approximations Using Minimum-Perimeter Polygons


                  MPP Algorithm


   Skeletons, Medial Axes, and Distance Transforms

12.3 Boundary Feature Descriptors

   Some Basic Boundary Descriptors

   Shape Numbers

   Fourier Descriptors

   Statistical Moments

12.4 Region Feature Descriptors

   Some Basic Descriptors

   Topological Descriptors


                  Statistical Approaches

                  Spectral Approaches

   Moment Invariants

12.5 Principal Components as Feature Descriptors

12.6 Whole-Image Features

   The Harris-Stephens Corner Detector

   Maximally Stable Extremal Regions (MSERs)

12.7 Scale-Invariant Feature Transform (SIFT)

   Scale Space

   Detecting Local Extrema

                  Finding the Initial Keypoints

                  Improving the Accuracy of Keypoint Locations

                  Eliminating Edge Responses

   Keypoint Orientation

   Keypoint Descriptors

   Summary of the SIFT Algorithm

13 Image Pattern Classification

13.1 Background

13.2 Patterns and Pattern Classes

   Pattern Vectors

   Structural Patterns

13.3 Pattern Classification by Prototype Matching

   Minimum-Distance Classifier

   Using Correlation for 2-D prototype matching

   Matching SIFT Features

   Matching Structural Prototypes

                  Matching Shape Numbers

                  String Matching

13.4 Optimum (Bayes) Statistical Classifiers

   Derivation of the Bayes Classifier

   Bayes Classifier for Gaussian Pattern Classes

13.5 Neural Networks and Deep Learning


   The Perceptron

   Multilayer Feedforward Neural Networks

                  Model of an Artificial Neuron

                  Interconnecting Neurons to Form a Fully Connected Neural Network

                  Forward Pass Through a Feedforward Neural Network

                  The Equations of a Forward Pass

                  Matrix Formulation

   Using Backpropagation to Train Deep Neural Networks

                  The Equations of Backpropagation

                  Matrix Formulation

13.6 Deep Convolutional Neural Networks

   A Basic CNN Architecture

                  Basics of How a CNN Operates

                  Neural Computations in a CNN

                  Multiple Input Images

   The Equations of a Forward Pass Through a CNN

   The Equations of Backpropagation Used to Train CNNs

13.7 Some Additional Details of Implementation




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