Digital Image Processing: Mathematical and Computational Methods: Woodhead Publishing Series in Electronic and Optical Materials
Autor J M Blackledgeen Limba Engleză Paperback – 29 noi 2005
- Provides mathematical methods required to describe images, image formation and different imaging systems
- Outlines the principle techniques used for processing digital images
- Relates the methods of processing and interpreting digital images to the ‘physics’ of imaging systems
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Specificații
ISBN-13: 9781898563495
ISBN-10: 1898563497
Pagini: 824
Dimensiuni: 156 x 234 x 45 mm
Greutate: 1.25 kg
Editura: ELSEVIER SCIENCE
Seria Woodhead Publishing Series in Electronic and Optical Materials
Locul publicării:United Kingdom
ISBN-10: 1898563497
Pagini: 824
Dimensiuni: 156 x 234 x 45 mm
Greutate: 1.25 kg
Editura: ELSEVIER SCIENCE
Seria Woodhead Publishing Series in Electronic and Optical Materials
Locul publicării:United Kingdom
Cuprins
- About the Author
- Foreword
- Preface
- Acknowledgements
- Notation
- Alphabetical
- Greek
- Operators
- Glossary
- Mathematical and Statistical
- Computer Science
- Organizational and Standards
- Introduction
- Imaging Science
- Signals and Images
- Image Formation
- Image Information
- Image Analysis
- Digital Image Processing
- Fundamental Problems
- About this Book
- Summary of Important Results
- Part
I:
Mathematical
and
Computational
Background
- Chapter
1:
Vector
Fields
- 1.1 Scalar Fields
- 1.2 Vector Fields
- 1.3 The Divergence Theorem
- 1.4 Summary of Important Results
- Chapter
2:
2D
Fourier
Theory
- 2.1 The 2D Complex Fourier Series
- 2.2 The 2D Delta Function
- 2.3 The 2D Fourier Transform
- 2.4 Physical Representation
- 2.5 The Spectrum
- 2.6 Definitions and Notation
- 2.7 Some Important Results
- 2.8 Some Important Theorems
- 2.9 Convolution and Correlation
- 2.10 Convolution and Correlation Theorems
- 2.11 Other Integral Transforms
- 2.12 Discussion
- 2.13 Summary of Important Results
- Chapter
3:
The
2D
DFT,
FFT
and
FIR
Filter
- 3.1 The Discrete Fourier Transform
- 3.2 The Sampling Theorem
- 3.3 The Discrete Spectrum of a Digital Image
- 3.4 The Fast Fourier Transform
- 3.5 The Imaging Equation and Convolution in 2D
- 3.6 The Finite Impulse Response Filter
- 3.7 Origin of the Imaging Equation
- 3.8 Summary of Important Results
- Chapter
4:
Field
and
Wave
Equations
- 4.1 The Langevin Equation
- 4.2 Maxwell’s Equations
- 4.3 General Solution to Maxwell’s (Microscopic) Equations
- 4.4 The Macroscopic Maxwell’s Equations
- 4.5 EM Waves in a Homogeneous Medium
- 4.6 EM Waves in an Inhomogeneous Medium
- 4.7 Elastic Field Equations
- 4.8 Inhomogeneous Elastic Wave Equation
- 4.9 Acoustic Field Equations
- 4.10 Discussion
- 4.11 Summary of Important Results
- Chapter
5:
Green
Functions
- 5.1 Overview
- 5.2 Introduction to the Green Function
- 5.3 The Time Independent Wave Operator
- 5.4 Wavefields Generated by Sources
- 5.5 Time Dependent Green Function
- 5.6 Time Dependent Sources
- 5.7 Green Function Solution to Maxwell’s Equation
- 5.8 The Diffusion Equation
- 5.9 Green Function Solution to the Diffusion Equation
- 5.10 The Laplace and Poisson Equations
- 5.11 Discussion
- 5.12 Summary of Important Results
- Problems: Part I
- Chapter
1:
Vector
Fields
- Part
II:
Imaging
Systems
Modelling
- Chapter
6:
Scattering
Theory
- 6.1 The Schrödinger and Helmholtz Equations
- 6.2 Solution to the Helmholtz Equation
- 6.3 Examples of Born Scattering
- 6.4 Other Approximation Methods
- 6.5 The Born Series
- 6.6 Inverse Scattering
- 6.7 Surface Scattering Theory
- 6.8 Summary of Important Results
- Chapter
7:
Imaging
of
Layered
Media
- 7.1 Pulse-Echo Imaging
- 7.2 EM Imaging of a Layered Dielectric
- 7.3 Acoustic Imaging of a Layered Material
- 7.4 Side-band Systems and Demodulation
- 7.5 Some Applications
- 7.6 Case Study: Imaging the Ionosphere
- 7.7 Case Study: Radar Plasma Screening
- 7.8 Summary of Important Results
- Chapter
8:
Projection
Tomography
- 8.1 Basic Principles
- 8.2 Projection Tomography and Scattering Theory
- 8.3 The Radon Transform
- 8.4 Back-Projection PSF
- 8.5 The Central Slice Theorem
- 8.6 Numerical Methods
- 8.7 The Hough Transform
- 8.8 Non-separable Image Processing
- 8.9 Summary of Important Results
- Chapter
9:
Diffraction
Tomography
- 9.1 Diffraction Tomography using CW Fields
- 9.2 Pulse Mode Diffraction Tomography
- 9.3 The Diffraction Slice Theorem
- 9.4 Quantitative Diffraction Tomography
- 9.5 EM Diffraction Tomography
- 9.6 Case Study: Simulation of an Ultrasonic B-Scan
- 9.7 Summary of Important Results
- Chapter
10:
Synthetic
Aperture
Imaging
- 10.1 Synthetic Aperture Radar
- 10.2 Principles of SAR
- 10.3 Electromagnetic Scattering Model for SAR
- 10.4 Case Study: The ‘Sea Spikes’ Problem
- 10.5 Quantitative Imaging with SAR
- 10.6 Synthetic Aperture Sonar
- 10.7 Summary of Important Results
- Chapter
11:
Optical
Image
Formation
- 11.1 Optical Diffraction
- 11.2 The Fourier Transforming Properties of a Lens
- 11.3 Linear Systems
- 11.4 Images of Lines and Edges
- 11.5 Linearity of Optical Imaging Systems
- 11.6 Coherent Image Formation
- 11.7 Phase Contrast Imaging
- 11.8 Incoherent Image Formation
- 11.9 Coherent and Incoherent Optical Imaging
- 11.10 Optical Beams
- 11.11 The Paraxial Wave Equation
- 11.12 Holographic Imaging
- 11.13 Case Study: Digital Watermarking
- 11.14 Summary of Important Results
- Problems: Part II
- Chapter
6:
Scattering
Theory
- Part
III:
Digital
Image
Processing
Methods
- Chapter
12:
Image
Restoration
and
Reconstruction
- 12.1 Introduction
- 12.2 Image Restoration
- 12.3 The Inverse Filter
- 12.4 The Wiener Filter
- 12.5 The Power Spectrum Equalization Filter
- 12.6 The Matched Filter
- 12.7 Maximum Entropy Deconvolution
- 12.8 Constrained Deconvolution
- 12.9 Phase Reconstruction and Phase Imaging
- 12.10 Non-stationary Deconvolution
- 12.11 Discussion
- 12.12 Summary of Important Results
- Chapter
13:
Reconstruction
of
Band-limited
Images
- 13.1 The Gerchberg-Papoulis Method
- 13.2 Incorporation of a Priori Information
- 13.3 Example Demonstration and Applications
- 13.4 Error Reduction Algorithm
- 13.5 Discussion
- 13.6 Summary of Important Results
- Chapter
14:
Bayesian
Estimation
Methods
- 14.1 Introduction to Probability and Bayes Rule
- 14.2 The Maximum Likelihood Filter
- 14.3 The Maximum a Posteriori Filter
- 14.4 Super Resolution using Bayesian Methods
- 14.5 Summary of Important Results
- Chapter
15:
Image
Enhancement
- 15.1 Basic Transforms
- 15.2 Histogram Equalization
- 15.3 Homomorphic Filtering
- 15.4 Light Diffusion and the High Emphasis Filter
- 15.5 Noise Reduction
- 15.6 The Median Filter
- 15.7 Summary of Important Results
- Problems: Part III
- Chapter
12:
Image
Restoration
and
Reconstruction
- Part
IV:
Pattern
Recognition
and
Computer
Vision
- Chapter
16:
Segmentation
and
Edge
Detection
- 16.1 Correlation and the Auto-covariance Function
- 16.2 Thresholding
- 16.3 Edge Detection
- 16.4 Second Order Edge Detection
- 16.5 The Marr-Hildreth Method
- 16.6 Pixel Clustering
- 16.7 Clustering Tools
- 16.8 Hierarchical Data Structures
- 16.9 Summary of Important Results
- Chapter
17:
Statistical
Modelling
and
Analysis
- 17.1 Random Scattering Theory
- 17.2 Statistical Modelling Methods
- 17.3 Phase Distribution Analysis
- 17.4 Fully Coherent Scattering Processes
- 17.5 Statistical Moments
- 17.6 Noise and Statistical Tests
- 17.7 Texture Segmentation
- 17.8 Summary of Important Results
- Chapter
18:
Fractal
Images
and
Image
Processing
- 18.1 Introduction
- 18.2 Geometry and Dimension
- 18.3 Fractal Curves and Fractal Signals
- 18.4 Random Scaling Fractals and Texture
- 18.5 Methods of Computing the Fractal Dimension
- 18.6 The Fourier and Fractal Dimensions
- 18.7 Other Dimensions and Higher Order Fractals
- 18.8 The Information Dimension
- 18.9 The Lyapunov Dimension
- 18.10 Fractal Images and Mandelbrot Surfaces
- 18.11 Generalized Random Scaling Fractal (RSF) Models
- 18.12 Multi-Fractal Analysis
- 18.13 Case Study: Fractional Light Diffusion
- 18.14 Summary of Important Results
- Chapter
19:
Coding
and
Compression
- 19.1 The Reasons for Compression
- 19.2 Lossless Coding Methods
- 19.3 Lossy Coding Methods
- 19.4 Fractal Image Compression
- 19.5 Properties and Features
- 19.6 Improved Fractal Compression
- 19.7 Compression Conscious Operations
- 19.8 Fractal Texture Maps
- 19.9 Summary of Important Results
- Problems: Part IV
- Chapter
16:
Segmentation
and
Edge
Detection
- Summary
- Appendix
A:
Solutions
to
Problems
- Solutions to Problems: Part I
- Solutions to Part II
- Solutions to Problems: Part III
- Solutions to Problems: Part IV
- Appendix B: Supplementary Problems
- Appendix C: Fourier Transform of a Fractal
- Appendix
D:
I/O
and
Graphics
Utilities
- Reading and Writing Images to and From a Named Data File
- Displaying a Digital Image
- Index