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E-raamat: Fundamentals of Nonlinear Digital Filtering

(University of Northumbria, Newcastle upon Tyne, UK),
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Fundamentals of Nonlinear Digital FilteringSuurem pilt
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Explains and evaluates current methods and applications in nonlinear digital filtering, for professors, researchers, and applications engineers, as well as students of signal processing. Covers both theoretical and practical aspects in chapters on nonlinear signal processing, statistical preliminaries, and 30 nonlinear filtering approaches, with definitions, algorithms, and detailed examples. A final chapter discusses statistical analysis and optimization of nonlinear filters. Includes margin icons, exercises, and b&w photos and diagrams, plus 50 algorithms for readers' use. Assumes understanding of basic signal processing concepts. Annotation c. by Book News, Inc., Portland, Or.
PREFACE v(6)
List of Symbols and Abbreviations
xi
1 Nonlinear Signal Processing
1(20)
1.1 Signal Processing Model
1(2)
1.2 Signal and Noise Models
3(1)
1.3 Fundamental Problems in Noise Removal
3(15)
1.3.1 One-Dimensional Signals Corrupted by Additive White Gaussian Noise
4(3)
1.3.2 One-Dimensional Signals Corrupted by Impulsive Noise
7(3)
1.3.3 Mean and Median in Image Filtering
10(8)
1.3.4 Linear Versus Nonlinear Filtering
18(1)
1.4 Algorithms
18(3)
2 Statistical Preliminaries
21(28)
2.1 Random Variables and Distributions
21(12)
2.1.1 One-Dimensional Random Variables
21(7)
2.1.2 Random Vectors and Random Processes
28(3)
2.1.3 Probability Elements
31(1)
2.1.4 Transformations of Random Vectors
32(1)
2.2 Signal and Noise Models
33(3)
2.3 Estimation
36(9)
2.3.1 Point Estimation
36(3)
2.3.2 Maximum Likelihood Estimators
39(2)
2.3.3 M-Estimators
41(1)
2.3.4 L-Estimators
42(1)
2.3.5 R-Estimators
43(1)
2.3.6 Scale Estimation
44(1)
2.4 Some Useful Distributions
45(4)
3 1001 Solutions
49(164)
3.1 Trimmed Mean Filters
52(4)
3.1.1 Principles and Properties
52(2)
3.1.2 Impulse and Step Response
54(1)
3.1.3 Filtering Examples
54(2)
3.2 Other Trimmed Mean Filters
56(10)
3.2.1 Principles and Properties
56(3)
3.2.2 Impulse and Step Response
59(1)
3.2.3 Filtering Examples
60(6)
3.3 L-Filters
66(4)
3.3.1 Principles and Properties
66(1)
3.3.2 Impulse and Step Response
67(1)
3.3.3 Filtering Examples
68(2)
3.4 C-Filters (Ll-Filters)
70(3)
3.4.1 Principles and Properties
70(2)
3.4.2 Impulse and Step Response
72(1)
3.4.3 Filtering Examples
72(1)
3.5 Weighted Median Filters
73(5)
3.5.1 Principles and Properties
73(3)
3.5.2 Impulse and Step Response
76(1)
3.5.3 Filtering Examples
76(2)
3.6 Ranked-Order and Weighted Order Statistic Filters
78(10)
3.6.1 Principles and Properties
78(3)
3.6.2 Impulse and Step Response
81(1)
3.6.3 Filtering Examples
82(6)
3.7 Multistage Median Filters
88(9)
3.7.1 Principles and Properties
88(3)
3.7.2 Impulse and Step Response
91(1)
3.7.3 Filtering Examples
91(6)
3.8 Median Hybrid Filters
97(8)
3.8.1 Principles and Properties
97(3)
3.8.2 Impulse and Step Response
100(1)
3.8.3 Filtering Examples
100(5)
3.9 Edge-Enhancing Selective Filters
105(4)
3.9.1 Principles and Properties
105(4)
3.9.2 Impulse and Step Response
109(1)
3.9.3 Filtering Examples
109(1)
3.10 Rank Selection Filters
109(13)
3.10.1 Principles and Properties
109(10)
3.10.2 Impulse and Step Response
119(1)
3.10.3 Filtering Examples
119(3)
3.11 M-Filters
122(18)
3.11.1 Principles and Properties
122(10)
3.11.2 Impulse and Step Response
132(1)
3.11.3 Filtering Examples
133(7)
3.12 R-Filters
140(7)
3.12.1 Principles and Properties
140(2)
3.12.2 Impulse and Step Response
142(1)
3.12.3 Filtering Examples
143(4)
3.13 Weighted Majority with Minimum Range Filters
147(2)
3.13.1 Principles and Properties
147(1)
3.13.2 Impulse and Step Response
147(1)
3.13.3 Filtering Examples
148(1)
3.14 Nonlinear Mean Filters
149(14)
3.14.1 Principles and Properties
149(3)
3.14.2 Impulse and Step Response
152(1)
3.14.3 Filtering Examples
153(10)
3.15 Stack Filters
163(4)
3.15.1 Principles and Properties
163(3)
3.15.2 Impulse and Step Response
166(1)
3.15.3 Filtering Examples
167(1)
3.16 Generalizations of Stack Filters
167(5)
3.16.1 Principles and Properties
167(5)
3.16.2 Impulse and Step Response
172(1)
3.16.3 Filtering Examples
172(1)
3.17 Morphological Filters
172(7)
3.17.1 Principles and Properties
172(5)
3.17.2 Impulse and Step Response
177(1)
3.17.3 Filtering Examples
177(2)
3.18 Soft Morphological Filters
179(7)
3.18.1 Principles and Properties
179(5)
3.18.2 Impulse and Step Response
184(1)
3.18.3 Filtering Examples
184(2)
3.19 Polynomial Filters
186(3)
3.19.1 Principles and Properties
186(3)
3.19.2 Impulse and Step Response
189(1)
3.19.3 Filtering Examples
189(1)
3.20 Data-Dependent Filters
189(5)
3.20.1 Principles and Properties
189(4)
3.20.2 Impulse and Step Response
193(1)
3.20.3 Filtering Examples
193(1)
3.21 Decision-Based Filters
194(7)
3.21.1 Principles and Properties
194(6)
3.21.2 Impulse and Step Response
200(1)
3.21.3 Filtering Examples
200(1)
3.22 Iterative, Cascaded, and Recursive Filters
201(3)
3.22.1 Principles and Properties
201(2)
3.22.2 Impulse and Step Response
203(1)
3.22.3 Filtering Examples
203(1)
3.23 Some Numerical Measures of Nonlinear Filters
204(8)
3.24 Discussion
212(1)
4 STATISTICAL ANALYSIS AND OPTIMIZATION OF NONLINEAR FILTERS
213(34)
4.1 Methods Based on Order Statistics
213(9)
4.1.1 Joint Distributions of Order Statistics
215(1)
4.1.2 Analysis of L-Filters
216(3)
4.1.3 Optimization of L-Filters
219(3)
4.2 Stack Filters
222(20)
4.2.1 Output Distribution of a Stack Filter
222(5)
4.2.2 Joint Distribution of Two Stack Filters
227(4)
4.2.3 Output Moments of Stack Filters
231(2)
4.2.4 Rank Selection Probabilities of Stack Filters
233(4)
4.2.5 Optimization of Stack Filters
237(2)
4.2.6 Stack Filter Optimization in the Boolean Lattice
239(3)
4.3 Multistage and Hybrid Filters
242(3)
4.4 Discussion
245(2)
5 Exercises
247(16)
BIBLIOGRAPHY 263(10)
INDEX 273


Astola\, Jaakko; Kuosmanen\, Pauli
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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