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• Contents
• Preface
• Acknowledgments
• The Book Website
• The DIP4E Support Packages
• About the Authors
• 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
• Introduction to the Basic 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 (Lowpass) Spatial Filters
• Sharpening (Highpass) Spatial Filters
• Highpass, Bandreject, and Bandpass Filters from Lowpass Filters
• Combining Spatial Enhancement Methods
• 4 Filtering in the Frequency Domain
• Background
• Preliminary Concepts
• Sampling and the Fourier Transform of Sampled Functions
• The Discrete Fourier Transform of One Variable
• Extensions to Functions of Two Variables
• Some Properties of the 2-D DFT and IDFT
• The Basics of Filtering in the Frequency Domain
• Image Smoothing Using Lowpass Frequency Domain Filters
• Image Sharpening Using Highpass Filters
• Selective Filtering
• The Fast Fourier Transform
• 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 Using 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
• Color Image Smoothing and Sharpening
• Using Color in Image Segmentation
• Noise in Color Images
• Color Image Compression
• 7 Wavelet and Other Image Transforms
• Preliminaries
• Matrix-based Transforms
• Correlation
• Basis Functions in the Time-Frequency Plane
• Basis Images
• Fourier-Related Transforms
• Walsh-Hadamard Transforms
• Slant Transform
• Haar Transform
• Wavelet Transforms
• 8 Image Compression and Watermarking
• Fundamentals
• Huffman Coding
• Golomb Coding
• Arithmetic Coding
• LZW Coding
• Run-length Coding
• Symbol-based Coding
• Bit-plane Coding
• Block Transform Coding
• Predictive Coding
• Wavelet Coding
• Digital Image Watermarking
• 9 Morphological Image Processing
• Preliminaries
• Erosion and Dilation
• Opening and Closing
• The Hit-or-Miss Transform
• Some Basic Morphological Algorithms
• Morphological Reconstruction
• Summary of Morphological Operations on Binary Images
• Grayscale Morphology
• 10 Image Segmentation
• Fundamentals
• Point, Line, and Edge Detection
• Thresholding
• Segmentation by Region Growing and by Region Splitting and Merging
• Region Segmentation Using Clustering and Superpixels
• Region Segmentation Using Graph Cuts
• Segmentation Using Morphological Watersheds
• The Use of Motion in Segmentation
• 11 Feature Extraction
• Background
• Boundary Preprocessing
• Boundary Feature Descriptors
• Region Feature Descriptors
• Principal Components as Feature Descriptors
• Whole-Image Features
• Scale-Invariant Feature Transform (SIFT)
• 12 Image Pattern Classification
• Background
• Patterns and Pattern Classes
• Pattern Classification by Prototype Matching
• Optimum (Bayes) Statistical Classifiers
• Neural Networks and Deep Learning
• Deep Convolutional Neural Networks
• Some Additional Details of Implementation
• Bibliography
• Index
• Back Cover