基于PCNN的图像椒盐噪声滤除方法  

Filtering image impulse noise by using a PCNN image noise reduction technique

作　　者：闫海鹏[1] 吴玉厚[2] 

机构地区：[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

出　　处：《智能系统学报》2017年第2期272-278,共7页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金项目(51375317)

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。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 reduc- tion 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 im- age edge details were also protected.

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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]