BEMD和狼群算法的自适应PCNN图像去噪方法  

NOISE REMOVAL FOR IMAGE USING ADAPTIVE PULSE-COUPLED NEURAL NETWORK OPTIMIZED BY BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION AND GREY WOLF OPTIMIZATION

作　　者：杨虹 晋涛 申冲[2] 米康民 黄纯德 刘永鑫 Yang Hong;Jin Tao;Shen Chong;Mi Kangmin;Huang Chunde;Liu Yongxin(State Grid Shanxi Electric Power Research Institute,Taiyuan 030001,Shanxi,China;Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry,North University of China,Taiyuan 030051,Shanxi,China;State Grid Shanxi Electric Power Company,Taiyuan 030021,Shanxi,China)

机构地区：[1]国网山西省电力公司电力科学研究院,山西太原030001 [2]中北大学仪器科学与动态测试教育部重点实验室,山西太原030051 [3]国网山西省电力公司,山西太原030021

出　　处：《计算机应用与软件》2025年第4期251-256,共6页Computer Applications and Software

基　　金：国网山西省电力公司科技项目(52053018000T);国家自然科学基金项目(51603194)。

摘　　要：提出一种二维经验模态分解(Bidimensional Empirical Mode Decomposition,BEMD)和狼群算法(Grey Wolf Optimization,GWO)自适应脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)的复合图像去噪方法。通过BEMD将原始图像分解成多个二维固有模态函数分量和一个残余分量,用狼群算法对PCNN参数进行优化,对分解的各个分量进行去噪,并将去噪后的各分量进行重建得到去噪后的图像。主要优点包括:(1)有效确定PCNN关键参数,提高模型收敛速度;(2)有效解决高强度噪声的抑制问题;(3)通过对噪声点进行隔离并恢复原始像素点,最终使得图像细节信息得以完整保留。A hybrid image denoising method based on an adaptive pulsed-couple neural network(PCNN)optimized by bidimensional empirical mode decomposition(BEMD)and the grey wolf optimization(GWO)is proposed.The BEMD decomposed an original image into various bidimensional intrinsic mode functions and a residual,and the decomposed components would be denoised by PCNN optimized with GWO,respectively.The wolf pack algorithm was used to optimize the PCNN parameters.A denoised image was obtained after reconstructing the denoised components.The advantages of this method include:(1)Deter-mining the key parameters of PCNN effectively and improving the convergence speed of the model;(2)Effectively solving the problem of high intensity noise suppression;(3)Preserving the details of the original image completely by isolating the noise points and recovering the original pixels.

关 键 词：图像去噪 脉冲耦合神经网络 狼群算法 二维经验模态分解 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]