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 Preface 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 Reference 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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 References 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.
Description based on publisher supplied metadata and other sources.