The Essential Guide to Image Processing.
Title:
The Essential Guide to Image Processing.
Author:
Bovik, Alan C.
ISBN:
9780080922515
Personal Author:
Edition:
2nd ed.
Physical Description:
1 online resource (877 pages)
Contents:
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.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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