.png){kind=icon}
返回
A fast algorithm for designing stack filters
.png){kind=icon}
PRE
AI
DOI：10.1109/83.777083.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Stack filters are a class of nonlinear filters with excellent properties for signal restoration. Unfortunately, present algorithms for designing stack filters can only be used for small window sizes because of either their computational overhead or their serial nature. This paper presents a new adaptive algorithm for determining a stack filter that minimizes the mean absolute error criterion. The new algorithm retains the iterative nature of many current adaptive stack filtering algorithms, but significantly reduces the number of iterations required to converge to an optimal filter. This algorithm is faster than all currently available stack filter design algorithms, is simple to implement, and is shown in this paper to always converge to an optimal stack filter. Extensive comparisons between this new algorithm and all existing algorithms are provided. The comparisons are based both on the performance of the resulting filters and upon the time and space complexity of the algorithms, They demonstrate that the new algorithm has three advantages: it is faster than all other available algorithms; it can be used on standard workstations (SPARC 5 with 48MB) to design filters with windows containing 20 or more points; and, its highly parallel structure allows very fast implementations on parallel machines. This new algorithm allows cascades of stack filters to be designed; stack filters with windows containing 72 points have been designed in a matter of minutes under this new approach.
Keyword:
enhancement
estimation
median filters
non-linear filters
stack filters
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
暂无机构信息
引用论文
Effects of calcium binding on the internal dynamic properties of bovine brain calmodulin, studied by NMR and optical spectroscopy.png)
Biochemistry
BiochemistryIF0
PREAI
分享收藏分享收藏
查看更多内容