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E-raamat: Digital Image Processing and Analysis: Image Enhancement, Restoration and Compression

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(Southern Illinois University, Edwardsville, USA)
  • Formaat: 488 pages
  • Ilmumisaeg: 30-Dec-2022
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000789911
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Digital Image Processing and Analysis: Image Enhancement, Restoration and CompressionSuurem pilt
  • Formaat: 488 pages
  • Ilmumisaeg: 30-Dec-2022
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000789911
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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 book, Digital Image Enhancement, Restoration and Compression, focuses on human vision based imaging application development.

Preface xi
Acknowledgments xv
Author xvii
1 Digital Image Processing and Analysis
1(38)
1.1 Overview
1(1)
1.2 Image Processing and Human Vision
2(4)
1.3 Digital Imaging Systems
6(3)
1.4 Image Formation and Sensing
9(13)
1.4.1 Visible Light Imaging
11(3)
1.4.2 Imaging Outside the Visible Range of the EM Spectrum
14(4)
1.4.3 Acoustic Imaging
18(1)
1.4.4 Electron Imaging
19(1)
1.4.5 Laser Imaging
19(1)
1.4.6 Computer-Generated Images
19(3)
1.5 Image Representation
22(9)
1.5.1 Binary Images
22(1)
1.5.2 Gray-Scale Images
22(1)
1.5.3 Color Images
22(6)
1.5.4 Multispectral and Multiband Images
28(1)
1.5.5 Digital Image File Formats
29(2)
1.6 Key Points
31(3)
1.7 References and Further Reading
34(2)
References
35(1)
1.8 Exercises
36(3)
2 Image Processing Development Tools
39(48)
2.1 Introduction and Overview
39(1)
2.2 CVIPtools Windows GUI
39(11)
2.2.1 Image Viewer
41(1)
2.2.2 Analysis Window
42(2)
2.2.3 Enhancement Window
44(1)
2.2.4 Restoration Window
44(1)
2.2.5 Compression Window
45(1)
2.2.6 Utilities Window
46(2)
2.2.7 Help Window
48(2)
2.2.8 Development Tools
50(1)
2.3 CVIPlab for C/C++ Programming
50(14)
2.3.1 Toolkit, Toolbox Libraries and Memory Management in C/C++
59(1)
2.3.2 Image Data and File Structures
60(4)
2.4 The MATLAB CVIP Toolbox
64(18)
2.4.1 Help Files
65(2)
2.4.2 M-Files
67(1)
2.4.3 CVIPtools for MATLAB GUI
67(1)
2.4.4 CVIPlab for MATLAB
68(5)
2.4.5 Vectorization
73(1)
2.4.6 Using CVIPlab for MATLAB
74(4)
2.4.7 Adding a Function
78(2)
2.4.8 A Sample Batch Processing M-File
80(1)
2.4.9 VIPM File Format
80(2)
2.5 References and Further Reading
82(1)
References
82(1)
2.6 Introductory Programming Exercises
82(2)
2.7 Digital Image Processing and Human Vision Projects
84(3)
3 Digital Image Processing and Visual Perception
87(40)
3.1 Introduction
87(1)
3.2 Image Analysis
87(2)
3.2.1 Overview
87(1)
3.2.2 System Model
88(1)
3.3 Human Visual Perception
89(21)
3.3.1 The Human Visual System
89(6)
3.3.2 Spatial Frequency Resolution
95(5)
3.3.3 Brightness Adaptation and Perception
100(5)
3.3.4 Temporal Resolution
105(2)
3.3.5 Perception and Illusion
107(3)
3.4 Image Fidelity Criteria
110(7)
3.4.1 Objective Fidelity Measures
110(2)
3.4.2 Subjective Fidelity Measures
112(5)
3.5 Key Points
117(4)
3.6 References and Further Reading
121(1)
References
121(1)
3.7 Exercises
122(2)
3.8 Supplementary Exercises
124(3)
4 Discrete Transforms
127(48)
4.1 Introduction and Overview
127(5)
4.2 Fourier Transform
132(20)
4.2.1 The One-Dimensional Discrete Fourier Transform
135(4)
4.2.2 Two-Dimensional Discrete Fourier Transform
139(2)
4.2.3 Fourier Transform Properties
141(1)
4.2.3.1 Linearity
142(1)
4.2.3.2 Convolution
142(1)
4.2.3.3 Translation
142(1)
4.2.3.4 Modulation
142(1)
4.2.3.5 Rotation
143(1)
4.2.3.6 Periodicity
144(1)
4.2.3.7 Sampling and Aliasing
144(1)
4.2.4 Displaying the Discrete Fourier Spectrum
145(7)
4.3 Discrete Cosine Transform
152(2)
4.4 Discrete Walsh-Hadamard Transform
154(5)
4.5 Discrete Haar Transform
159(2)
4.6 Principal Components Transform
161(3)
4.7 Key Points
164(5)
4.8 References and Further Reading
169(1)
References
169(1)
4.9 Exercises
169(4)
4.10 Supplementary Exercises
173(2)
5 Transform Filters, Spatial Filters and the Wavelet Transform
175(36)
5.1 Introduction and Overview
175(1)
5.2 Lowpass Filters
175(7)
5.3 Highpass Filters
182(4)
5.4 Bandpass, Bandreject and Notch Filters
186(2)
5.5 Spatial Filtering via Convolution
188(7)
5.5.1 Lowpass Filtering in the Spatial Domain
190(1)
5.5.2 Highpass Filtering in the Spatial Domain
190(4)
5.5.3 Bandpass and Bandreject Filtering in the Spatial Domain
194(1)
5.6 Discrete Wavelet Transform
195(7)
5.7 Key Points
202(2)
5.8 References and Further Reading
204(1)
References
205(1)
5.9 Exercises
205(3)
5.10 Supplementary Exercises
208(3)
6 Image Enhancement
211(84)
6.1 Introduction and Overview
211(3)
6.2 Gray-Scale Modification
214(44)
6.2.1 Mapping Equations
214(8)
6.2.2 Histogram Modification
222(15)
6.2.3 Adaptive Contrast Enhancement
237(10)
6.2.4 Color
247(11)
6.3 Image Sharpening
258(11)
6.3.1 Highpass Filtering
258(1)
6.3.2 High-Frequency Emphasis (HFE)
258(3)
6.3.3 Directional Difference Filters ~
261(1)
6.3.4 Homomorphic Filtering
261(3)
6.3.5 Unsharp Masking
264(1)
6.3.6 Edge Detector-Based Sharpening Algorithms
264(5)
6.4 Image Smoothing
269(10)
6.4.1 Frequency Domain Smoothing
269(1)
6.4.2 Spatial Domain Smoothing
269(3)
6.4.3 Smoothing with Nonlinear Filters
272(7)
6.5 Key Points
279(6)
6.6 References and Further Reading
285(1)
References
285(1)
6.7 Exercises
286(6)
6.8 Supplementary Exercises
292(3)
7 Image Restoration and Reconstruction
295(98)
7.1 Introduction and Overview
295(1)
7.1.1 System Model
295(1)
7.2 Noise Models
296(11)
7.2.1 Noise Histograms
297(5)
7.2.2 Periodic Noise
302(1)
7.2.3 Estimation of Noise
303(4)
7.3 Noise Removal Using Spatial Filters
307(27)
7.3.1 Order Filters
307(6)
7.3.2 Mean Filters
313(9)
7.3.3 Adaptive Filters
322(12)
7.4 The Degradation Function
334(5)
7.4.1 The Spatial Domain - The Point Spread Function
334(3)
7.4.2 The Frequency Domain - The Modulation/Optical Transfer Function
337(1)
7.4.3 Estimation of the Degradation Function
337(2)
7.5 Frequency Domain Restoration Filters
339(14)
7.5.1 Inverse Filter
340(3)
7.5.2 Wiener Filter
343(2)
7.5.3 Constrained Least Squares Filter
345(1)
7.5.4 Geometric Mean Filters
346(2)
7.5.5 Adaptive Filtering
348(1)
7.5.6 Bandpass, Bandreject and Notch Filters
348(3)
7.5.7 Practical Considerations
351(2)
7.6 Geometric Transforms
353(11)
7.6.1 Spatial Transforms
353(3)
7.6.2 Gray-Level Interpolation
356(3)
7.6.3 The Geometric Restoration Procedure
359(1)
7.6.4 Geometric Restoration with CVIPtools
359(5)
7.7 Image Reconstruction
364(6)
7.7.1 Reconstruction Using Backprojections
364(3)
7.7.2 The Radon Transform
367(2)
7.7.3 The Fourier-Slice Theorem and Direct Fourier Reconstruction
369(1)
7.8 Key Points
370(11)
7.9 References and Further Reading
381(2)
References
382(1)
7.10 Exercises
383(6)
7.11 Supplementary Exercises
389(4)
8 Image Compression
393(70)
8.1 Introduction and Overview
393(8)
8.1.1 Compression System Model
396(5)
8.2 Lossless Compression Methods
401(12)
8.2.1 Huffman Coding
404(3)
8.2.2 Golomb-Rice Coding
407(1)
8.2.3 Run-Length Coding
408(4)
8.2.4 Lempel-Ziv-Welch Coding
412(1)
8.2.5 Arithmetic Coding
412(1)
8.3 Lossy Compression Methods
413(35)
8.3.1 Gray-Level Run-Length Coding
415(2)
8.3.2 Block Truncation Coding
417(6)
8.3.3 Vector Quantization
423(5)
8.3.4 Differential Predictive Coding
428(7)
8.3.5 Model-Based and Fractal Compression
435(2)
8.3.6 Transform Coding
437(6)
8.3.7 Hybrid and Wavelet Methods
443(5)
8.4 Key Points
448(5)
8.5 References and Further Reading
453(2)
References
454(1)
8.6 Exercises
455(4)
8.7 Supplementary Exercises
459(4)
Index 463
Dr. Scott E Umbaugh is a Distinguished Research Professor of Electrical and Computer Engineering and Graduate Program Director for the Department of Electrical and Computer Engineering at Southern Illinois University Edwardsville (SIUE). He is also the Director of the Computer Vision and Image Processing (CVIP) Laboratory at SIUE. He has been teaching computer vision and image processing, as well as computer and electrical engineering design, for over 30 years. His professional interests include computer vision and image processing education, research and development of both human and computer vision applications, and engineering design education.

Prior to his academic career, Dr. Umbaugh worked as a computer design engineer and project manager in the avionics and telephony industries. He has been a computer imaging consultant since 1986 and has provided consulting services for the aerospace, medical and manufacturing industries with projects ranging from automatic identification of defects in microdisplay chips to analysis of thermographic images for clinical diagnosis of brain disease. He has performed research and development for projects funded by the National Institutes of Health, the National Science Foundation and the U. S. Department of Defense.

Dr. Umbaugh is author or co-author of numerous technical papers, two edited books, and multiple editions of his textbooks on computer vison and image processing. His books are used at academic and research organizations throughout the world. He has served on editorial boards and as a reviewer for a variety of IEEE journals and has evaluated research monographs and textbooks in the imaging field.

Dr. Umbaugh received his B.S.E. degree with honors from Southern Illinois University Edwardsville in 1982, M.S.E.E. in 1987 and Ph.D. in 1990 from the Missouri University of Science and Technology, where he was a Chancellor's Fellow. He is a senior member of the Institute of Electrical and Electronic Engineers (IEEE), a member of Sigma Xi and the International Society for Optical Engineering (SPIE). Dr. Umbaugh is also the primary developer of the CVIPtools software package and the associated CVIP Matlab Toolbox.
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