This thesis studies the optimization problem of stack filters based on pth-order error criterion using global optimization algorithms. And simulates the optimal stack filters based on 3th-order error norm and 4th-order error norm separately.
Using the algorithm for generating positive Boolean function randomly and the optimization model of stack filters based on MAE criteria, optimal stack filters are searched by simple genetic algorithms.
This paper analyses the VLSI implementation scheme of stack filters,then analyses the logistic structure based on the usage of 3x3 Moving Window, the analyses and resolve some problem appear during this course.
Stack filters belong to the class of non-linear filters and include the well-known median filter, weighted median filters, order statistic filters and weighted order statistic filters.
The use of the EE-optimal and MAE-optimal Boolean and stack filters in the sequential prediction structure is considered, under different instances: global-optimal, block-optimal, adaptive-size-block-optimal and multiresolution.
Prediction Capabilities of Boolean and Stack Filters for Lossless Image Compression
Flat morphological operators, also called stack filters, are the natural extension of increasing set operators to grey-level images.
Stack filters are widely used nonlinear filters based on threshold decomposition and positive Boolean functions.
The degeneration mechanism of characteristics of infrared (Ge/SiO) stack filters is discussed based on experimental results. We propose a method to prevent such degeneration of characteristics. By using the method, high-quality filters have been obtained which can withstand immersion tests in water and solutions of salt or acid.
Based on the generalized threshold decomposition property,a modified binarytree search algorithm used to adjust the threshold levels is obtained. A parallel pipelinedarchitecture of stack filtering is proposed.
In this paper we first give two important properties of stack filters, that is,stability and self-inclusion. Secondly we study a class of more general filters, socalled window filters, and we prove that a window filter with self-inclusion must be a stack filter.