Fundamentals of Nonlinear Digital Filtering is the first book of its kind, presenting and evaluating current methods and applications in nonlinear digital filtering. Written for professors, researchers, and application engineers, as well as for serious students of signal processing, this is the only book available that functions as both a reference handbook and a textbook. Solid introductory material, balanced coverage of theoretical and practical aspects, and dozens of examples provide you with a self-contained, comprehensive information source on nonlinear filtering and its applications.
Table of Contents
Nonlinear Signal Processing: Signal Processing Model. Signal and Noise Models. Fundamental Problems in Noise Removal. Algorithms. Statistical Preliminaries: Random Variables and Distributions. Signal and Noise Models. Estimation. Some Useful Distributions. 1001 Solutions: Trimmed Mean Filters. Other Trimmed Mean Filters. L-Filters. C-Filters (Ll-Filters). Weighted Median Filters. Ranked-Order and Weighted Order Statistic Filters. Multistage Median Filters. Median Hybrid Filters. Edge-Enhancing Selective Filters. Rank Selection Filters. M-Filters. R-Filters. Weighted Majority with Minimum Range Filters. Nonlinear Mean Filters. Stack Filters. Generalizations of Stack Filters. Morphological Filters. Soft Morphological Filters. Polynomial Filters. Data-Dependent Filters. Decision-Based Filters. Iterative, Cascaded, and Recursive Filters. Some Numerical Measures of Nonlinear Filters. Discussion. Statistical Analysis and Optimization of Nonlinear Filters: Methods Based on Order Statistics. Stack Filters. Multistage and Hybrid Filters. Discussion. Exercises. Bibliography. Index.
Astola\, Jaakko; Kuosmanen\, Pauli