Digital Image Enhancement, Restoration and Compression focuses on human vision-based imaging application development. Examples include making poor images look better, the development of advanced compression algorithms, special effects imaging for motion pictures and the restoration of satellite images distorted by atmospheric disturbance. This book presents a unique engineering approach to the practice of digital imaging, which starts by presenting a global model to help gain an understanding of the overall process, followed by a breakdown and explanation of each individual topic. Topics are presented as they become necessary for understanding the practical imaging model under study, which provides the reader with the motivation to learn about and use the tools and methods being explored.
The book includes chapters on imaging systems and software, the human visual system, image transforms, image filtering, image enhancement, image restoration, and image compression. Numerous examples, including over 700 color images, are used to illustrate the concepts discussed. Readers can explore their own application development with any programming language, including C/C++, MATLAB, Python and R, and software is provided for both the Windows/C/C++ and MATLAB environments.
The book can be used by the academic community in teaching and research, with over 1,000 PowerPoint slides and a complete solutions manual to the over 230 included problems. It can also be used for self-study by those involved with application development, whether they are engineers, scientists or artists. The new edition has been extensively updated and includes numerous problems and programming exercises that will help the reader and student develop their skills.
The prerequisites for the book are an interest in the field, a basic background in computers and a basic math background provided in an undergraduate science or engineering program. Knowledge of the C family of programming languages, including C, C++ and C#, and/or MATLAB experience will be necessary for those intending to develop algorithms at the application level. Some background in signal and system theory is required for those intending to gain a deep understanding of the sections on transforms and compression. However, the book is written so that those without this background can learn to use the tools and achieve a conceptual understanding of the material.
Author(s): Scott E Umbaugh
Series: Digital Image Processing and Analysis
Edition: 4
Publisher: CRC Press
Year: 2022
Language: English
Pages: 470
Preface
Acknowledgments
Author
1 Digital Image Processing and Analysis
1.1 Overview
1.2 Image Processing and Human Vision
1.3 Digital Imaging Systems
1.4 Image Formation and Sensing
1.4.1 Visible Light Imaging
1.4.2 Imaging Outside the Visible Range of the EM Spectrum
1.4.3 Acoustic Imaging
1.4.4 Electron Imaging
1.4.5 Laser Imaging
1.4.6 Computer-Generated Images
1.5 Image Representation
1.5.1 Binary Images
1.5.2 Gray-Scale Images
1.5.3 Color Images
1.5.4 Multispectral and Multiband Images
1.5.5 Digital Image File Formats
1.6 Key Points
1.7 References and Further Reading
References
1.8 Exercises
2 Image Processing Development Tools
2.1 Introduction and Overview
2.2 CVIPtools Windows GUI
2.2.1 Image Viewer
2.2.2 Analysis Window
2.2.4 Restoration Window
2.2.5 Compression Window
2.2.6 Utilities Window
2.2.7 Help Window
2.2.8 Development Tools
2.3 CVIPlab for C/C++ Programming
2.3.1 Toolkit, Toolbox Libraries and Memory Management in C/C++
2.3.2 Image Data and File Structures
2.4 The MATLAB CVIP Toolbox
2.4.1 Help Files
2.4.2 M-Files
2.4.3 CVIPtools for MATLAB GUI
2.4.4 CVIPlab for MATLAB
2.4.5 Vectorization
2.4.6 Using CVIPlab for MATLAB
2.4.7 Adding a Function
2.4.8 A Sample Batch Processing M-File
2.4.9 VIPM File Format
2.5 References and Further Reading
References
2.6 Introductory Programming Exercises
2.7 Digital Image Processing and Human Vision Projects
3 Digital Image Processing and Visual Perception
3.1 Introduction
3.2 Image Analysis
3.2.1 Overview
3.2.2 System Model
3.3 Human Visual Perception
3.3.1 The Human Visual System
3.3.2 Spatial Frequency Resolution
3.3.3 Brightness Adaptation and Perception
3.3.4 Temporal Resolution
3.3.5 Perception and Illusion
3.4 Image Fidelity Criteria
3.4.1 Objective Fidelity Measures
3.4.2 Subjective Fidelity Measures
3.5 Key Points
3.6 References and Further Reading
References
3.7 Exercises
3.8 Supplementary Exercises
4 Discrete Transforms
4.1 Introduction and Overview
4.2 Fourier Transform
4.2.1 The One-Dimensional Discrete Fourier Transform
4.2.2 Two-Dimensional Discrete Fourier Transform
4.2.3 Fourier Transform Properties
4.2.3.1 Linearity
4.2.3.2 Convolution
4.2.3.3 Translation
4.2.3.4 Modulation
4.2.3.5 Rotation
4.2.3.6 Periodicity
4.2.3.7 Sampling and Aliasing
4.2.4 Displaying the Discrete Fourier Spectrum
4.3 Discrete Cosine Transform
4.4 Discrete Walsh–Hadamard Transform
4.5 Discrete Haar Transform
4.6 Principal Components Transform
4.7 Key Points
4.8 References and Further Reading
References
4.9 Exercises
4.10 Supplementary Exercises
5 Transform Filters, Spatial Filters and the Wavelet Transform
5.1 Introduction and Overview
5.2 Lowpass Filters
5.3 Highpass Filters
5.4 Bandpass, Bandreject and Notch Filters
5.5 Spatial Filtering via Convolution
5.5.1 Lowpass Filtering in the Spatial Domain
5.5.2 Highpass Filtering in the Spatial Domain
5.5.3 Bandpass and Bandreject Filtering in the Spatial Domain
5.6 Discrete Wavelet Transform
5.7 Key Points
5.8 References and Further Reading
References
5.9 Exercises
5.10 Supplementary Exercises
6 Image Enhancement
6.1 Introduction and Overview
6.2 Gray-Scale Modification
6.2.1 Mapping Equations
6.2.2 Histogram Modification
6.2.3 Adaptive Contrast Enhancement
6.2.4 Color
6.3 Image Sharpening
6.3.1 Highpass Filtering
6.3.2 High-Frequency Emphasis (HFE)
6.3.3 Directional Difference Filters
6.3.4 Homomorphic Filtering
6.3.5 Unsharp Masking
6.3.6 Edge Detector–Based Sharpening Algorithms
6.4 Image Smoothing
6.4.1 Frequency Domain Smoothing
6.4.2 Spatial Domain Smoothing
6.4.3 Smoothing with Nonlinear Filters
6.5 Key Points
6.6 References and Further Reading
References
6.7 Exercises
6.8 Supplementary Exercises
7 Image Restoration and Reconstruction
7.1 Introduction and Overview
7.1.1 System Model
7.2 Noise Models
7.2.1 Noise Histograms
7.2.2 Periodic Noise
7.2.3 Estimation of Noise
7.3 Noise Removal Using Spatial Filters
7.3.1 Order Filters
7.3.2 Mean Filters
7.3.3 Adaptive Filters
7.4 The Degradation Function
7.4.1 The Spatial Domain – The Point Spread Function
7.4.2 The Frequency Domain – The Modulation/Optical Transfer Function
7.4.3 Estimation of the Degradation Function
7.5 Frequency Domain Restoration Filters
7.5.1 Inverse Filter
7.5.2 Wiener Filter
7.5.3 Constrained Least Squares Filter
7.5.4 Geometric Mean Filters
7.5.5 Adaptive Filtering
7.5.6 Bandpass, Bandreject and Notch Filters
7.5.7 Practical Considerations
7.6 Geometric Transforms
7.6.1 Spatial Transforms
7.6.2 Gray-Level Interpolation
7.6.3 The Geometric Restoration Procedure
7.6.4 Geometric Restoration with CVIPtools
7.7 Image Reconstruction
7.7.1 Reconstruction Using Backprojections
7.7.2 The Radon Transform
7.7.3 The Fourier-Slice Theorem and Direct Fourier Reconstruction
7.8 Key Points
7.9 References and Further Reading
References
7.10 Exercises
7.11 Supplementary Exercises
8 Image Compression
8.1 Introduction and Overview
8.1.1 Compression System Model
8.2 Lossless Compression Methods
8.2.1 Huffman Coding
8.2.2 Golomb-Rice Coding
8.2.3 Run-Length Coding
8.2.4 Lempel–Ziv–Welch Coding
8.2.5 Arithmetic Coding
8.3 Lossy Compression Methods
8.3.1 Gray-Level Run-Length Coding
8.3.2 Block Truncation Coding
8.3.3 Vector Quantization
8.3.4 Differential Predictive Coding
8.3.5 Model-Based and Fractal Compression
8.3.6 Transform Coding
8.3.7 Hybrid and Wavelet Methods
8.4 Key Points
