Digital Image Processing, 4th edition

Published by Pearson (January 1, 2022) © 2016

  • Rafael C. Gonzalez University of Tennessee
  • Richard E. Woods MedData Interactive
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For courses in Image Processing and Computer Vision.

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 students and faculty containing, solutions, image databases, and sample code.   


Support materials for this title can be found here: http://www.imageprocessingplace.com

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

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