Digital Image Processing using SCILAB (eBook)

Dr. Rohit Thanki obtained his Ph.D. in Multibiometric System Security using CS Theory and Watermarking from C. U. Shah University, Wadhwan city, Gujarat, India in 2017. His area of research interest is Digital Watermarking, Biometrics System, Security, Compressive Sensing, Pattern Recognition and Image Processing. He has published 3 books, 4 book chapters and 28 research papers to his credit in refereed & indexed journals, and conference at international and national level. His international recognition includes his professional memberships & services in refereed organizations, programme committees and reviewer for journals published by IEEE, Elsevier, Taylor & Francis, Springer, IGI-Global etc. Dr. Ashish Kothari obtained his Ph.D. in Digital Video Watermarking from JJT University, Rajasthan, India in 2013. He is working as an Associate professor and Head of Electronics and Communication Engineering, Atmiya Institute of Technology and Science, Rajkot, Gujarat, India. He is also recognized Ph.D. supervisor at Gujarat Technological University, Ahmedabad, Gujarat, India. His area of research interest is Image Processing, Video Processing, Digital Watermarking and Signal Processing. He has published 1 book, 1 book chapters and more than 25 research papers to his credit in refereed & indexed journals, and conferences at international and national level. His international recognition includes his professional memberships & services in refereed organizations, programme committees and reviewer for journals.

Preface 5
Acknowledgments 7
Contents 8
List of Figures 12
Chapter 1: Introduction 18
1.1 Introduction 18
1.2 Types of Signals 19
1.2.1 One-Dimensional Signals 19
1.2.2 Two-Dimensional Signals 20
1.2.3 Three-Dimensional Signals 20
1.2.4 Multi-Dimensional Signals 20
1.3 A Digital Image and Its Processing 21
1.4 Information of Scilab Software 26
1.4.1 How to Obtain and Install Scilab 27
1.4.2 How to Install the Image Processing Toolbox in Scilab 27
1.5 Areas of Image Processing Covered in the Book 34
Bibliography 35
Chapter 2: Image Enhancement in the Spatial Domain 36
2.1 Introduction 36
2.2 Image Enhancement by Point Processing 36
2.2.1 Identity Transformation 37
2.2.2 Image Negative 38
2.2.3 Contrast Stretching 39
2.2.4 Contrast Thresholding 41
2.2.5 Gray Level Slicing 42
2.2.5.1 Gray Level Slicing Without Background 43
2.2.5.2 Gray Level Slicing with Background 44
2.2.6 Bit Plane Slicing 45
2.2.7 Log Transformation 46
2.2.8 Power Low Transformation 48
2.3 Histogram 49
2.3.1 Histogram Processing 52
2.3.1.1 Histogram Stretching 52
Example of Histogram Stretching 53
Solution 53
2.3.1.2 Histogram Equalization 54
Example of Histogram Equalization 55
Solution for Histogram Equalization 55
2.3.1.3 Histogram Matching (Specification) 57
2.4 Image Enhancement by Neighborhood Processing: Spatial Domain Filters 58
2.4.1 Concept of Frequency in Images 59
2.4.2 Low-Pass Average Filter 60
2.4.3 Low-Pass Median Filter (Order Statistic Filter) 61
2.4.4 High-Pass Filter 63
2.4.5 High-Boost Filter 64
2.5 Image Enhancement Using Arithmetic/Logical Operations 65
Bibliography 67
Chapter 3: Image Enhancement in the Frequency Domain 68
3.1 Introduction 68
3.2 Fourier Transform 69
3.2.1 Important Properties of Discrete Fourier Transform (DFT) 71
3.3 Low-Pass Frequency Domain Filter 73
3.3.1 Ideal Low-Pass Filter (LPF) 73
3.3.2 Butterworth LPF 75
3.3.3 Gaussian LPF 77
3.4 High-Pass Frequency Domain Filter 78
3.4.1 Ideal High-Pass Filter (HPF) 79
3.4.2 Butterworth HPF 79
3.4.3 Gaussian HPF 81
3.5 Unsharp Masking 83
3.5.1 Homomorphic Filtering 84
Bibliography 86
Chapter 4: Image Restoration 87
4.1 Introduction 87
4.2 Image Degradation and Restoration Process 88
4.3 Noise Models 89
4.3.1 Gaussian Noise 89
4.3.2 Rayleigh Noise 91
4.3.3 Erlang/Gamma Noise 91
4.3.4 Exponential Noise 93
4.3.5 Uniform Noise 95
4.3.6 Salt and Pepper Noise 95
4.4 Periodic Noise and Estimation of Noise Parameters 96
4.5 Image Restoration: Spatial Filtering 98
4.5.1 Mean Filters 98
4.5.1.1 Arithmetic Mean Filter 99
4.5.1.2 Geometric Mean Filter 100
4.5.1.3 Harmonic Mean Filter 100
4.5.1.4 Contra Harmonic Mean Filter 102
4.5.2 Order Statistics Filters 104
4.5.2.1 Median Filter 104
4.5.2.2 Min Filter 105
4.5.2.3 Max Filter 106
4.5.2.4 Mid-Point Filter 108
4.5.2.5 Alpha Trimmed Mean Filter 109
4.5.3 Adaptive Filters 110
4.6 Wiener Filtering 112
Bibliography 114
Chapter 5: Morphological Image Processing 115
5.1 Introduction 115
5.1.1 Structuring Elements: Hits and Fits 115
5.2 Fundamental Morphology Operations 116
5.2.1 Erosion 116
5.2.2 Dilation 118
5.3 Compound Morphology Operations 119
5.3.1 Opening 119
5.3.2 Closing 122
5.4 Hit or Miss Transform 123
5.5 Some Morphological Algorithms 125
5.5.1 Boundary Extraction 126
5.5.2 Thinning 127
5.5.3 Thickening 128
Bibliography 129
Chapter 6: Image Segmentation 130
6.1 Introduction 130
6.2 Point Detection 130
6.3 Line Detection 131
6.4 Edge Detection 134
6.4.1 Sobel Edge Detector 134
6.4.2 Prewitt Edge Detector 136
6.4.3 Roberts Edge Detector 138
6.4.4 Laplacian of a Gaussian (LOG) Edge Detector 140
6.4.5 Canny Edge Detector 142
6.5 Thresholding 143
Bibliography 144
Chapter 7: Color Image Processing 145
7.1 Color Image Representation in SCILAB 145
7.2 Conversion of an RGB (Red, Green, Blue) Image into Other Spaces 146
7.2.1 NTSC Color Space 146
7.2.2 YCbCr Color Space 148
7.2.3 HSV (Hue, Saturation, Value) Color Space 148
7.2.4 CMY (Cyan, Magenta, Yellow) Color Space 149
7.3 Basic Operations for a Color Image 151
7.3.1 Histogram of a Color Image 151
7.3.2 Color Image Smoothing 152
7.3.3 Color Image Sharping 153
7.3.4 Color Edge Detection 155
Bibliography 156
Chapter 8: Applications of Digital Image Processing 157
8.1 Introduction 157
8.2 Copyright Protection 157
8.3 Image Compression 160
8.4 Fog Removal 160
8.5 Template Matching 160
8.6 Image Mosaicing 162
Bibliography 163
Index 165