The Essential Guide to Image Processing.

Bovik, Alan C.
San Diego : Elsevier Science & Technology, 2009.
1 online resource (877 pages)
2nd ed.

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Image processing -- Digital techniques.
Image processing.
Electronic books.
A complete introduction to the basic and intermediate concepts of image processing from the leading people in the field A CD-ROM contains 70 highly interactive demonstration programs with user friendly interfaces to provide a visual presentation of the concepts Up-to-date content, including statistical modeling of natural, anistropic diffusion, image quality and the latest developments in JPEG 2000 This comprehensive and state-of-the art approach to image processing gives engineers and students a thorough introduction, and includes full coverage of key applications: image watermarking, fingerprint recognition, face recognition and iris recognition and medical imaging. To help learn the concepts and techniques, the book contains a CD-ROM of 70 highly interactive visual demonstrations. Key algorithms and their implementation details are included, along with the latest developments in the standards. "This book combines basic image processing techniques with some of the most advanced procedures. Introductory chapters dedicated to general principles are presented alongside detailed application-orientated ones. As a result it is suitably adapted for different classes of readers, ranging from Master to PhD students and beyond." - Prof. Jean-Philippe Thiran, EPFL, Lausanne, Switzerland "Al Bovik's compendium proceeds systematically from fundamentals to today's research frontiers. Professor Bovik, himself a highly respected leader in the field, has invited an all-star team of contributors. Students, researchers, and practitioners of image processing alike should benefit from the Essential Guide." - Prof. Bernd Girod, Stanford University, USA "This book is informative, easy to read with plenty of examples, and allows great flexibility in tailoring a course on image processing or analysis." - Prof. Pamela Cosman, University of California, San Diego, USA * A
complete and modern introduction to the basic and intermediate concepts of image processing - edited and written by the leading people in the field * An essential reference for all types of engineers working on image processing applications * A CD-ROM contains 70 highly interactive demonstration programs with user friendly interfaces to provide a visual presentation of the concepts * Up-to-date content, including statistical modelling of natural, anisotropic diffusion, image quality and the latest developments in JPEG 2000.
Front Cover
The Essential Guide to Image Processing
Copyright Page
Table of Contents
About the Author
Chapter 1. Introduction to Digital Image Processing
1.1 Types of Images
1.2 Scale of Images
1.3 Dimension of Images
1.4 Digitization of Images
1.5 Sampled Images
1.6 Quantized Images
1.7 Color Images
1.8 Size of Image Data
1.9 Objectives of this Guide
1.10 Organization of the Guide
Chapter 2. The SIVA Image Processing Demos
2.1 Introduction
2.2 LabVIEW for Image Processing
2.2.1 The LabVIEW Development Environment
2.2.2 Image Processing and Machine Vision in LabVIEW
2.3 Examples from the SIVA Image Processing Demos
2.4 Conclusions
Chapter 3. Basic Gray Level Image Processing
3.1 Introduction
3.2 Notation
3.3 Image Histogram
3.4 Linear Point Operations on Images
3.4.1 Additive Image Offset
3.4.2 Multiplicative Image Scaling
3.4.3 Image Negative
3.4.4 Full-Scale Histogram Stretch
3.5 Nonlinear Point Operations on Images
3.5.1 Logarithmic Point Operations
3.5.2 Histogram Equalization
3.5.3 Histogram Shaping
3.6 Arithmetic Operations Between Images
3.6.1 Image Averaging for Noise Reduction
3.6.2 Image Differencing for Change Detection
3.7 Geometric Image Operations
3.7.1 Nearest Neighbor Interpolation
3.7.2 Bilinear Interpolation
3.7.3 Image Translation
3.7.4 Image Rotation
3.7.5 Image Zoom
Chapter 4. Basic Binary Image Processing
4.1 Introduction
4.2 Image Thresholding
4.3 Region Labeling
4.3.1 Region Labeling Algorithm
4.3.2 Region Counting Algorithm
4.3.3 Minor Region Removal Algorithm
4.4 Binary Image Morphology
4.4.1 Logical Operations
4.4.2 Windows
4.4.3 Morphological Filters
4.4.4 Morphological Boundary Detection.
4.5 Binary Image Representation and Compression
4.5.1 Run-Length Coding
4.5.2 Chain Coding
Chapter 5. Basic Tools for Image Fourier Analysis
5.1 Introduction
5.2 Discrete-Space Sinusoids
5.3 Discrete-Space Fourier Transform
5.3.1 Linearity of DSFT
5.3.2 Inversion of DSFT
5.3.3 Magnitude and Phase of DSFT
5.3.4 Symmetry of DSFT
5.3.5 Translation of DSFT
5.3.6 Convolution and the DSFT
5.4 2D Discrete Fourier Transform (DFT)
5.4.1 Linearity and Invertibility of DFT
5.4.2 Symmetry of DFT
5.4.3 Periodicity of DFT
5.4.4 Image Periodicity Implied by DFT
5.4.5 Cyclic Convolution Property of the DFT
5.4.6 Linear Convolution Using the DFT
5.4.7 Computation of the DFT
5.4.8 Displaying the DFT
5.5 Understanding Image Frequencies and the DFT
5.5.1 Frequency Granularity
5.5.2 Frequency Orientation
5.6 Related Topics in this Guide
Chapter 6. Multiscale Image Decompositions and Wavelets
6.1 Overview
6.2 Pyramid Representations
6.2.1 Decimation and Interpolation
6.2.2 Gaussian Pyramid
6.2.3 Laplacian Pyramid
6.3 Wavelet Representations
6.3.1 Filter Banks
6.3.2 Wavelet Decomposition
6.3.3 Discrete Wavelet Bases
6.3.4 Continuous Wavelet Bases
6.3.5 More on Wavelet Image Representations
6.3.6 Relation to Human Visual System
6.3.7 Applications
6.4 Other Multiscale Decompositions
6.4.1 Undecimated Wavelet Transform
6.4.2 Wavelet Packets
6.4.3 Geometric Wavelets
6.5 Conclusion
Chapter 7. Image Noise Models
7.1 Summary
7.2 Preliminaries
7.2.1 What is Noise?
7.2.2 Notions of Probability
7.3 Elements of Estimation Theory
7.4 Types of Noise and Where They Might Occur
7.4.1 Gaussian Noise
7.4.2 Heavy Tailed Noise
7.4.3 Salt and Pepper Noise
7.4.4 Quantization and Uniform Noise.
7.4.5 Photon Counting Noise
7.4.6 Photographic Grain Noise
7.5 CCD Imaging
7.6 Speckle
7.6.1 Speckle in Coherent Light Imaging
7.6.2 Atmospheric Speckle
7.7 Conclusions
Chapter 8. Color and Multispectral Image Representation and Display
8.1 Introduction
8.2 Preliminary Notes on Display of Images
8.3 Notation and Prerequisite Knowledge
8.3.1 Practical Sampling
8.3.2 One-Dimensional Discrete System Representation
8.3.3 Multidimensional System Representation
8.4 Analog Images as Physical Functions
8.5 Colorimetry
8.5.1 Color Sampling
8.5.2 Discrete Representation of Color-Matching
8.5.3 Properties of Color-Matching Functions
8.5.4 Notes on Sampling for Color Aliasing
8.5.5 A Note on the Nonlinearity of the Eye
8.5.6 Uniform Color Spaces
8.6 Sampling of Color Signals and Sensors
8.7 Color I/O Device Calibration
8.7.1 Calibration Definitions and Terminology
8.7.2 CRT Calibration
8.7.3 Scanners and Cameras
8.7.4 Printers
8.7.5 Calibration Example
8.8 Summary and Future Outlook
Chapter 9. Capturing Visual Image Properties with Probabilistic Models
9.1 The Gaussian Model
9.2 The Wavelet Marginal Model
9.3 Wavelet Local Contextual Models
9.4 Discussion
Chapter 10. Basic Linear Filtering with Application to Image Enhancement
10.1 Introduction
10.2 Impulse Response, Linear Convolution, and Frequency Response
10.3 Linear Image Enhancement
10.3.1 Moving Average Filter
10.3.2 Ideal Lowpass Filter
10.3.3 Gaussian Filter
10.4 Discussion
Chapter 11. Multiscale Denoising of Photographic Images
11.1 Introduction
11.2 Distinguishing Images from Noise in Multiscale Representations
11.3 Subband Denoising-A Global Approach
11.3.1 Band Thresholding
11.3.2 Band Weighting.
11.4 Subband Coefficient Denoising-A Pointwise Approach
11.4.1 Coefficient Thresholding
11.4.2 Coefficient Weighting
11.5 Subband Neighborhood Denoising-Striking a Balance
11.5.1 Neighborhood Thresholding
11.5.2 Neighborhood Weighting
11.6 Statistical Modeling for Optimal Denoising
11.6.1 The Bayesian View
11.6.2 Empirical Bayesian Methods
11.7 Conclusions
Chapter 12. Nonlinear Filtering for Image Analysis and Enhancement
12.1 Introduction
12.2 Weighted Median Smoothers and Filters
12.2.1 Running Median Smoothers
12.2.2 Weighted Median Smoothers
12.2.3 Weighted Median Filters
12.3 Image Noise Cleaning
12.4 Image Zooming
12.5 Image Sharpening
12.6 Conclusion
Chapter 13. Morphological Filtering
13.1 Introduction
13.2 Morphological Image Operators
13.2.1 Morphological Filters for Binary Images
13.2.2 Morphological Filters for Gray-level Images
13.2.3 Universality of Morphological Operators
13.2.4 Median, Rank, and Stack Filters
13.2.5 Algebraic Generalizations of Morphological Operators
13.3 Morphological Filters for Image Enhancement
13.3.1 Noise Suppresion and Image Smoothing
13.3.2 Connected Filters for Smoothing and Simplification
13.3.3 Contrast Enhancement
13.4 Morphological Operators for Template Matching
13.4.1 Morphological Correlation
13.4.2 Binary Object Detection and Rank Filtering
13.4.3 Hit-Miss Filter
13.5 Morphological Operators for Feature Detection
13.5.1 Edge Detection
13.5.2 Peak/Valley Blob Detection
13.6 Design Approaches for Morphological Filters
13.7 Conclusions
Chapter 14. Basic Methods for Image Restoration and Identification
14.1 Introduction
14.2 Blur Models
14.2.1 No Blur
14.2.2 Linear Motion Blur
14.2.3 Uniform Out-of-Focus Blur.
14.2.4 Atmospheric Turbulence Blur
14.3 Image Restoration Algorithms
14.3.1 Inverse Filter
14.3.2 Least-Squares Filters
14.3.3 Iterative Filters
14.3.4 Boundary Value Problem
14.4 Blur Identification Algorithms
14.4.1 Spectral Blur Estimation
14.4.2 Maximum Likelihood Blur Estimation
Chapter 15. Iterative Image Restoration
15.1 Introduction
15.2 Iterative Recovery Algorithms
15.3 Spatially Invariant Degradation
15.3.1 Degradation Model
15.3.2 Basic Iterative Restoration Algorithm
15.3.3 Convergence
15.3.4 Reblurring
15.3.5 Experimental Results
15.4 Matrix-Vector Formulation
15.4.1 Basic Iteration
15.4.2 Least-Squares Iteration
15.4.3 Constrained Least-Squares Iteration
15.4.4 Spatially Adaptive Iteration
15.5 Use of Constraints
15.5.1 Experimental Results
15.6 Additional Considerations
15.6.1 Other Forms of the Iterative Algorithm
15.6.2 Hierarchical Bayesian Image Restoration
15.6.3 Blind Deconvolution
15.6.4 Additional Applications
15.7 Discussion
Chapter 16. Lossless Image Compression
16.1 Introduction
16.2 Basics of Lossless Image Coding
16.3 Lossless Symbol Coding
16.3.1 Basic Concepts from Information Theory
16.3.2 Context-Based Entropy Coding
16.3.3 Huffman Coding
16.3.4 Arithmetic Coding
16.3.5 Lempel-Ziv Coding
16.3.6 Elias and Exponential-Golomb Codes
16.4 Lossless Coding Standards
16.4.1 The JBIG and JBIG2 Standards
16.4.2 The Lossless JPEG Standard
16.4.3 The JPEG2000 Standard
16.5 Other Developments in Lossless Coding
16.5.1 CALIC
16.5.2 Perceptually Lossless Image Coding
Chapter 17. JPEG and JPEG2000
17.1 Introduction
17.2 Lossy JPEG Codec Structure
17.2.1 Encoder Structure
17.2.2 Decoder Structure
17.3 Discrete Cosine Transform.
17.4 Quantization.
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Bovik, Alan C.
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Print version: Bovik, Alan C. The Essential Guide to Image Processing