[1]闫海鹏,吴玉厚.基于PCNN的图像椒盐噪声滤除方法[J].智能系统学报,2017,12(02):272-278.[doi:10.11992/tis.201605027]
 YAN Haipeng,WU Yuhou.Filtering image impulse noise by using a PCNN image noise reduction technique[J].CAAI Transactions on Intelligent Systems,2017,12(02):272-278.[doi:10.11992/tis.201605027]
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基于PCNN的图像椒盐噪声滤除方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年02期
页码:
272-278
栏目:
出版日期:
2017-04-25

文章信息/Info

Title:
Filtering image impulse noise by using a PCNN image noise reduction technique
作者:
闫海鹏1 吴玉厚2
1. 沈阳建筑大学 机械工程学院, 辽宁 沈阳 110168;
2. 沈阳建筑大学 高档石材数控加工装备与技术国家地方联合工程实验室, 辽宁 沈阳 110168
Author(s):
YAN Haipeng1 WU Yuhou2
1. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China;
2. National-Local Joint Engineering Laboratory of High-Grade Stone Numerical Control Machining Equipments and Technology, Shenyang Jianzhu University, Shenyang 110168, China
关键词:
图像降噪脉冲耦合神经网络突触链接强度阈值函数分辨力
Keywords:
image noise reductionpulse coupling neural networksynaptic link strengththreshold functionresolving power
分类号:
TP391
DOI:
10.11992/tis.201605027
摘要:
传统的降噪方法在图像降噪之后会损坏图像的部分边缘细节信息,致使图像的轮廓变得模糊不清。为了达到更好的图像降噪效果,提出一种改变突触链接强度和改进阈值函数的脉冲耦合神经网络的图像降噪方法。该方法将基本脉冲耦合神经网络模型进行简化,使突触链接强度自适应取值,将阈值函数改进为分段的衰减函数,从而提高对图像不同灰度值的分辨力,并根据神经元与其周围神经元点火时间差定位噪声点,提高了算法对噪声点的辨识精确度,进而实现更好的降噪效果。实验结果表明,改进方法准确地辨识出了图像的椒盐噪声点,并且能够有效去除噪声点,同时很好地保护图像边缘细节。
Abstract:
Traditional methods for image noise reduction typically damage the edges and details of an image, blur image contours, and thereby make them indistinct after image noise reduction is complete. To achieve better results in image noise reduction, we propose a pulse coupling neural network (PCNN) image noise reduction method based on a modified synaptic link strength and a modified threshold function. We simplified the basic PCNN model and adaptively changed the synaptic link strength value; further, we improved the threshold function by using a segmented attenuation function so as to improve the resolving power for different gray values of the given images. We improved the accuracy of our algorithm for identifying noise by positioning noise points according to the difference of firing times between the neuron and its surrounding neurons. Using this approach, we achieved better noise reduction results; our experimental results showed that our proposed method was able to accurately identify image impulse noise points and effectively remove these noise points. Further, through subjective evaluation, we observed that image edge details were also protected.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-5-26;改回日期:。
基金项目:国家自然科学基金项目(51375317).
作者简介:闫海鹏,男,1987年生,博士研究生,主要研究方向为脆性材料加工、噪声检测与去除。主持完成内蒙古自治区研究生科研创新项目1项,参与国家自然科学基金项目2项。发表学术论文11篇;吴玉厚,男,1955年生,教授,博士生导师,博士,主要研究方向为陶瓷零件精密加工制造技术、数控机床高速主轴系统关键技术。主持完成国家级、省部级科研课题20余项。获得国家技术发明二等奖1项,国家科技进步二等奖1项,国家专利金奖1项,国家专利优秀奖1项,辽宁省技术发明一等奖2项,辽宁省科技进步一等奖1项,省部级科技奖二等奖7项。国家发明专利10项。发表学术论文387篇,被SCI、EI检索132篇,出版专著8部。
通讯作者:吴玉厚. E-mail:wuyh@sjzu.edu.cn.
更新日期/Last Update: 1900-01-01