A Concise Introduction to Image Processing using C++

266 Pages·2008·5.457 MB·English

by  Meiqing Wang, Choi-Hong Lai

A Concise
Introduction to
Image Processing
using
C++
© 2009 by Taylor & Francis Group, LLC
C8970_C000.indd 1 10/10/08 7:10:14 PM
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Editors
Choi-Hong Lai Frédéric Magoulès
School of Computing and Applied Mathematics and
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University of Greenwich Ecole Centrale Paris
Editorial Advisory Board
Mark Ainsworth Peter Jimack
Mathematics Department School of Computing
Strathclyde University University of Leeds
Todd Arbogast Takashi Kako
Institute for Computational Department of Computer Science
Engineering and Sciences The University of Electro-Communications
The University of Texas at Austin
Peter Monk
Craig C. Douglas Department of Mathematical Sciences
Computer Science Department University of Delaware
University of Kentucky

Francois-Xavier Roux
Ivan Graham ONERA
Department of Mathematical Sciences
University of Bath Arthur E.P. Veldman
Institute of Mathematics and Computing Science
University of Groningen

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© 2009 by Taylor & Francis Group, LLC
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© 2009 by Taylor & Francis Group, LLC
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A Concise
Introduction to
Image Processing
using
C++
Meiqing Wang
Choi-Hong Lai
© 2009 by Taylor & Francis Group, LLC
C8970_C000.indd 5 10/10/08 7:10:15 PM
CRC Press
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Table of Contents
Preface xv
Chapter 1 ■ Basic Concepts of Images 1
1.1 AnAlogue SIgnAlS 1
1.2 DIgItAl SIgnAlS 3
1.2.1 Sampling 4
1.2.2 Quantisation 5
1.3 grey-SCAle ImAgeS 6
1.3.1 Resolution 6
1.3.2 Grey Levels 6
1.4 Colour ImAgeS 7
1.4.1 The RGB Colour Model 9
1.4.2 The YIQ Colour Model 10
1.4.3 The YUV Model 11
1.4.4 The HSI Model 12
1.4.4.1 Conversion from the RGB Model to
the HSI Model 14
1.4.4.2 Conversion from the HSI Model to
the RGB Model 14
1.4.5 The CMY Model 16
1.5 ImAge StorAge FormAtS 17
1.5.1 The BMP Format 17
1.5.2 The RAW Format 18
vii
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viii < table of Contents
1.5.3 The JPEG format 18
1.5.4 The GIF Format 19
1.6 VIDeO 19
1.7 exerCISeS 20
1.8 reFerenCeS 20
1.9 PartIaL CODe examPLeS 21
Project 1-1: Convert an 8-bit grey-scale
image to a binary image 21
Project 1-2: Convert a 24-bit colour image
to its red channel image 22
Project 1-3: Convert an 8-bit colour image
to a grey-scale image 25
Chapter 2 ■ Basic Image Processing tools 29
2.1 COrreLatIOn OPeratIOn anD
COnVOLutIOn OPeratIOn 30
2.1.1 Correlation Operations 30
2.1.2 Convolution Operations 32
2.2 FOurIer tranSFOrm 37
2.2.1 Continuous Fourier Transform 37
2.2.1.1 One-Dimensional Continuous
Fourier Transform 37
2.2.1.2 Two-Dimensional Continuous
Fourier Transform 38

2.2.2 The Discrete Fourier Transform 38
2.2.3 Properties of the Discrete
Fourier Transform 39
2.2.4 The Fast Fourier Transform 41
2.3 the DISCrete COSIne tranSFOrm 42
2.4 the gaBOr tranSFOrm 43
2.5 the WaVeLet tranSFOrm 44
2.5.1 The Continuous Wavelet Transform 44
2.5.2 The Discrete Wavelet Transform 45
© 2009 by Taylor & Francis Group, LLC
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table of Contents < ix
2.6 Further reaDIng: OrthOgOnaLIty
anD COmPLeteneSS 46
2.6.1 Orthogonality 47
2.6.2 Completeness 47
2.7 exerCISeS 48
2.8 reFerenCeS 49
2.9 PartIaL CODe examPLeS 49
Project 2-1: Fourier Transformation 49
Project 2-2: DCT Transformation 58
Project 2-3: Wavelet Transformation and
the inverse wavelet transformation 60
Chapter 3 ■ Preprocessing techniques for Images 65
3.1 PIxeL BrIghtneSS (grey-LeVeL)
tranSFOrmatIOnS 66
3.1.1 Image Enhancement Based on Histogram 66
3.1.1.1 Histogram 66
3.1.1.2 Histogram Equalisation 66
3.1.2 Contrast Stretching 69
3.1.2.1 Linear Transform 70
3.1.2.2 The Limiting Linear Transform 70
3.2 COnCePtS anD mODeLS OF
Image PrePrOCeSSIng 71
3.3 Image SmOOthIng 73

3.3.1 Spatial-Domain Methods 73
3.3.1.1 Neighbourhood-Averaging
Methods 73
3.3.1.2 Threshold-Averaging Methods 75
3.3.1.3 Gaussian Filtering 75
3.3.1.4 Median Filtering 76
3.3.1.5 Weighted Median Filtering 77
3.3.2 Frequency-Domain Methods 78
3.3.2.1 Ideal Low-Pass Filtering 78
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x < table of Contents
3.3.2.2 Trapezoidal Low-Pass Filtering 79
3.3.2.3 Butterworth Low-Pass Filtering 79
3.4 Image enhanCement 80
3.4.1 Gradient 80
3.4.2 Gradient Image 81
3.4.3 Gradient Operators 81
3.4.3.1 Roberts Operator 83
3.4.3.2 Prewitt Operator 83
3.4.3.3 Sobel Operator 83
3.4.3.4 Laplacian Operator 84
3.4.4 High-Pass Filtering 85
3.4.4.1 Ideal High-Pass Filtering 85
3.4.4.2 Trapezoidal High-Pass Filtering 87
3.4.4.3 Butterworth High-Pass Filtering 87
3.5 Image reStOratIOn 87
3.5.1 Image Degradation Model 87
3.5.2 Image Restoration Based on
the Degradation Model 89
3.5.2.1 Unconstrained Conditional Restoration 89
3.5.2.2 Constrained Conditional Restoration 90
3.5.3 Inverse Filtering 91
3.5.4 Wiener Filtering 92
3.5.5 Geometric Rectification 93

3.5.5.1 Spatial Geometric Transforms 94
3.5.5.2 Confirmation of Pixel Intensities 96
3.6 PrOCeSSIng methODS uSIng PartIaL
DIFFerentIaL equatIOnS 97
3.6.1 Diffusion-Based Models 98
3.6.1.1 The Heat Conduction Model 98
3.6.1.2 The Anisotropic Diffusion Model 98
3.6.2 TV-Based Models 102
3.6.3 Discrete Formats of PDE Models 103
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