期刊文献+

一种最近邻像素值的极大概率滤波算法 被引量:1

A great probability filter algorithm based nearest-neighbor pixels' gray
下载PDF
导出
摘要 图像增强是图像处理中重要的过程,其目的是消除噪声和凸显感兴趣特征。针对当前经典中值滤波算法不足,提出一种最近邻像素值的极大概率滤波算法(GPF),该算法将掩膜窗覆盖下的所有像素值视为一个像素值集,并定义评价各个元素对整个集合的影响度关系,同时提供计算这种影响度的方法,确定选值准则。最后在VC++6平台上实现该算法并与当前存在的中值滤波算法相比较。实验证明,关于最近邻像素值的极大概率滤波算法能够有效消除噪声和保留细节。 Image enhancing is an important process when image processing ,which aims at denoising and highlighting interesting parts in an image.Directing at the disadvantage of traditional median filter ,presents the Great Probability Filter Algorithm (GPF) based nearest-neighbor pixels' gray value.The Algorithm treats the pixels' value as a value set,and defines a Influence Degree between an element and the set,and determine the GPF Criterion.And finally realize the GPF algorithm in VC++6 and compare it with traditional median filter algorithm .Experiments show that the GPF algorithm can denoise image effectively and attain detail in image.
出处 《电子设计工程》 2014年第7期181-183,187,共4页 Electronic Design Engineering
基金 国家自然科学基金资助项目(50875089)
关键词 消除噪声 滤波算法 极大概率 影响度 中值滤波 image denosing filtering algorithm great probability influence degree median filter
  • 相关文献

参考文献8

二级参考文献63

共引文献394

同被引文献10

  • 1Qian Y H, Liang J Y, Pedrycz W, et al. Positive Approxima- tion: An Accelerator for Attribute Reduction in Rough Set Theory[J]. Artificial Intelligence ,2010,174(9-10):597-618.
  • 2Yang X B ,Yu D J,Yang J Y,et al. Dominance-based Rough Set Approach to Incomplete Interval-valued Information System[J]. Data & Knowledge Engineering, 2009,68 ( 11 ) : 1331-1347.
  • 3Yang X B,Yang Y,Wu C,et al. Dominance-based Rough Set Approach and Knowledge Reductions in Incomplete Ordered Information System[Jl. Information Sciences,2008, 178(4):1219-1234.
  • 4Qian Y H ,Liang J Y ,Pedrycz W ,et al. An Efficient Acceler- ator for Attribute Reduction From Incomplete Data in Rough Set Framework[J]. Pattern Recognition,2011,44 (8):1658- 1670.
  • 5Zeng Z L,Zhang H J,Zhang R,et al. A Novel Feature Selection Method Considering Feature Interaction[J]. Pattern Recognition, 2015,48:2656-2666.
  • 6Ding J Y,Song S J,Gupta J,et al. An Improved Iterated Greedy Algorithm With a Tabu-based Reconstruction Strate- gy for the No-wait Flowshop Scheduling Problem[J]. Applied Soft Computing, 2015,30(5 ):604-613.
  • 7Kohavi R,John G H. Wrappers for Feature Subset Selection [J]. Artificial Intelligence, 1997,97(1-2):273-324.
  • 8Hu Q H, Pedrycz W, Yu D R, Lang J. Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics ,2010,40( 1 ):137-150.
  • 9景永霞,苟和平,冯百明,李勇.不均衡数据集中KNN分类器样本裁剪算法[J].科学技术与工程,2013,21(16):4720-4723. 被引量:2
  • 10刘轩,王卫红,唐晓斌,李鹏.遗传算法在SAR图像目标鉴别特征选择上的应用[J].电子科技,2014,27(5):140-144. 被引量:2

引证文献1

二级引证文献7

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部