|本期目录/Table of Contents|

[1]陈宏希.一种基于噪声连接分量的自适应层次中值滤波算法[J].工业仪表与自动化装置,2016,(03):25-30.
　CHEN Hongxi.An adaptive layered median filter algorithm based on noised pixels connected components[J].Industrial Instrumentation & Automation,2016,(03):25-30.
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一种基于噪声连接分量的自适应层次中值滤波算法(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2016年03期
页码:
25-30
栏目:
出版日期:
2016-06-15

文章信息/Info

Title:
An adaptive layered median filter algorithm based on noised pixels connected components
文章编号:
1000-0682(2016)03-0000-00
作者:
陈宏希
（兰州石化职业技术学院 电子电气工程系，兰州 730060）
Author(s):
CHEN Hongxi
（Department of Electric & Electronic Engineering, Lanzhou Petrochemical College of Vocational Technology, Lanzhou 730060, China）
关键词:
中值滤波; 椒盐噪声; 标记处理; 连接分量; 自适应层次中值滤波
Keywords:
median filter;  salt &;  pepper noise;  label processing;  connected component;  adaptive layered median filter
分类号:
TP14
DOI:
-
文献标志码:
A
摘要:
针对椒盐噪声污染图像的降噪，提出一种基于噪声连接分量的自适应层次中值滤波算法。首先根据椒盐噪声像素点的邻接关系和分布特点，对噪声定位图像实施标记处理；然后计算各连接分量集合中元素的个数，将噪声定位图像分层，得到层次噪声定位图像；最后按照由低层到高层的顺序，逐层对噪声污染图像进行标准中值滤波处理，而标准中值滤波的窗口大小则依据连接分量自适应的进行调整，最终得到滤波结果。将该算法与标准中值滤波、自适应中值滤波进行仿真实验，并与其他中值滤波算法就指标PSNR进行比较，均表明该算法降噪效果优良。
Abstract:
A new median filter algorithm, which based on noised pixels connected components, is proposed to restore images corrupted by salt-and-pepper noise. While achieve this algorithm, there is a label processing, which bases on the salt-and-pepper noised pixels adjacency relations and distribution characteristics, is carried out for the noised pixels position image at first, then the element numbers each of the connected components sets are calculated from the label matrix. According to the element numbers, the noised pixels position image is segmented into layered images which called layered noised pixels position image. At the end, from lower to the higher layer, a continuous of iterated standard median filters are applied on the salt-and-pepper noise corrupted image, by reaches the highest layer, the algorithm is over and the result of filtering is got. Extensive simulations are carried out to validate the proposed algorithm. Experimental results show superiorities both visually and quantitatively compared to the standard median filter, adaptive median filter and other median filters to the index of PSNR for the removed of salt-and-pepper noise at different densities, and the proposed algorithm is more valid and advantageous.

参考文献/References:

[1] Anil K J. Fundamentals of Digital Image Processing[M]. NJ: Upper Saddle River, Prentice Hall, 1989.

[2] Mikan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing, Analysis,and Machine Vision[M]. 3thed. Toronto:Thomson Learning,2008.
[3] 张兆礼,赵春晖,梅晓丹.现代图像处理技术及Matlab实现[M].北京:人民邮电出版社,2001.
[4] Mamta Juneja, Rajni Mohana. An improved adaptive median filtering method for impulse noise detection[J]. International Journal of Recent Trends in Enfineerinf,2009, 1(1): 274-278.?
[5] Rafael C.Gonzalez, Richard E.Woods, Steven L.Eddins. Digital Image Processing Using Matalb[M],2thed,Gatemark, LLC, 2009.
[6] 王晓凯,李锋.改进的自适应中值滤波[J].计算机工程与应用,2010,46(3):175-176,218.
[7] Sukomal Mehta,Sanjeev Dhull.Fuzzy based median filter for gray-scale images[J].International Journal of Engineering Science and Advanced Technology,2012,2(4): 975-980.
[8] Jagadish H.Pujar.Robust fuzzy median filter for impulse noise reduction of gray scale images[J].World Academy of Science, Engineering and Technology,2010(40): 635-639.
[9] 倪臣敏,叶懋冬,陈孝春.一种改进的自适应中值滤波算法[J].中国图形图象学报,2006,11(5):672-678.
[10] B.Deka,D.Baishnab.A noncausal linear prediction based switching median filter for the removal of salt and pepper noise[J].ACEEE Int.J.on Signal & Image Processing,2012, 3(1):69-75.
[11] 吕宁,张宇,孙广明.基于GF(28)域相似函数的椒盐噪声滤波算法[J].计算机工程与应用,2010,46(13): 158-160,228.
[12] 曾宪佑,黄佐华.一种新型的自适应模糊中值滤波算法[J].计算机工程与应用,2014,50(17):134-136,199.
[13] 陈从平,王健,秦武.高密度椒盐噪声图像开关自适应滤波算法[J].激光与红外,2011,41(7):817-821.

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备注/Memo

备注/Memo:
收稿日期：2015-10-12
基金项目：2010年度甘肃省属高校基本科研业务费专项资金资助“往复式压缩机状态监测及RCM技术研究”（甘财教[2010]176）
作者简介：陈宏希（1970），男，陕西岐山人，2002年毕业于兰州大学信息科学与工程学院，硕士，副教授，主要研究方向为图像处理，模式识别，专家系统。
更新日期/Last Update: 1900-01-01