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Logic-based Nonlinear Image Processing
Author(s): Stephen Marshall
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Book Description
This text provides insight into the design of optimal image processing operators for implementation directly into digital hardware. Starting with simple restoration examples and using the minimum of statistics, the book provides a design strategy for a wide range of image processing applications. The text is aimed principally at electronics engineers and computer scientists, but will also be of interest to anyone working with digital images.
Book Details
Date Published: 4 December 2006
Pages: 162
ISBN: 9780819463432
Volume: TT72
Table of Contents
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Acknowledgments xiii
Chapter 1 Introduction 1
References 7
Chapter 2 What Is a Logic-Based Filter? 9
2.1 Error Criterion 11
2.2 Filter Constraints 12
2.3 Window Constrain t 13
2.4 Translation Invariance 13
2.5 Filter Windows 13
2.6 Filter Design 14
2.7 Minimizing the MAE 15
2.8 Summary 18
References 18
Chapter 3 How Accurate Is the Logic-Based Filter? 19
3.1 Optimum Filter Error 19
3.2 Other Applications 23
3.2.1 Edge noise 23
3.2.2 Simple optical character recognition 25
3.2.3 Resolution conversion 26
3.3 Summary 27
References 28
Chapter 4 How Do You Train the Filter for a Task? 29
4.1 Effect of Window Size 31
4.2 Training Errors 36
4.3 In Defense of Training Set Approaches 40
4.4 Summary 41
References 42
Chapter 5 Increasing Filters and Mathematical Morphology 43
5.1 Constraints on the Filter Function 43
5.2 Statistical Relevance 54
5.3 Summary 55
References 56
Chapter 6 The Median Filter and Its Variants 57
6.1 The Grayscale Median as a Special Case of
a Generalized WOS Filter 57
6.2 Binary WOS Filters 59
6.3 Positive and Negative Medians 59
6.4 Weighted Median Filters 60
6.5 Optimum Design of Weighted Rank and Median Filters 61
6.6 Weight-Monotonic Property 64
6.7 Design of Weighted Median Filters 66
6.8 Summary 70
References 70
Chapter 7 Extension to Grayscale 73
7.1 Stack Filters 73
7.2 Grayscale Morphology 79
7.3 Computational Morphology for Beginners 81
7.4 Elemental Erosion 82
7.5 Aperture Filters 88
7.6 Grayscale Applications 93
7.6.1 Film archive restoration 93
7.6.2 Removal of sensor noise 94
7.6.3 Image deblurring 96
7.7 Summary 98
References 98
Chapter 8 Grayscale Implementation 101
8.1 Grayscale Training Issues 101
8.1.1 Envelope filtering 101
8.2 Hardware Implementation 104
8.3 Stack Filter 107
8.4 Grayscale Morphology 112
8.5 Computational Morphology and Aperture Filters 113
8.6 Efficient Architecture for Computational
Morphology and Aperture Filters 115
8.7 Summary 119
References 119
Chapter 9 Case Study: Noise Removal from Astronomical Images 121
9.1 CCD Noise in Astronomical and Solar Images 121
9.2 Soft Morphological Filters 123
9.3 Results 127
9.3.1 Creation of a training set 127
9.3.2 Training 128
9.3.3 Application to real images 133
9.4 Hardware Implementation 134
9.5 Summary 138
References 138
Chapter 10 Conclusions 141
Reference 144
Index 145
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