This book discusses the problems that should be solved by nonlinear digital filtering and analyzes the drawbacks of the basic solutions, the mean and the median filters. It explores the intimate connection between filtering and statistical estimation.

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. NTI/Sales Copy