基于遗传蚁群优化的PCNN改进中值滤波图像去噪方法  

Improved PCNN Median Filter Image Denoising Method Based on Genetic Ant Colony Optimization

作　　者：朱雪梅 ZHU Xuemei

机构地区：[1]四川省内江市中心血站,四川内江641099

出　　处：《科技创新与应用》2024年第20期1-7,共7页Technology Innovation and Application

摘　　要：为实现数字图像自适应去噪,提出一种基于遗传蚁群算法(GACA)优化的脉冲耦合神经网络(PCNN)改进中值滤波混合图像去噪方法(GACA-PCNN-MF)。通过将遗传算法(GA)和蚁群算法(ACO)相结合使GA的计算结果用于增强ACO早期信息素,最终使ACO在正反馈机制中加速优化PCNN关键参数,然后使用优化后的PCNN改进中值滤波技术进行图像去噪处理。通过实验分析和定量计算与现有其他图像去噪技术对比,结果表明,提出的GACA-MF改进混合图像去噪方法的效果优于分别使用中值滤波算法和PCNN算法。可见,利用自适应的方式优化网络参数可以尽可能发掘PCNN的最大潜能。In order to realize adaptive image denoising,animproved pulse coupled neural network(PCNN)median filter image denoising method based on genetic ant colony algorithm(GACA)is proposed.Through the combination of genetic algorithm(GA)and ant colony optimization(ACO)algorithm,the calculation results of GA are used to enhance the early pheromones of ACO,and finally make ACO accelerate the optimization of the key parameters of PCNN in the positive feedback mechanism;then,PCNN was used to optimizemedian filtering technology for image denoising.Through experimental analysis and quantitative calculation,as well as comparison with other existing image denoising techniques,the results show that the proposed GACA-MF improved hybrid image denoising method is better than using median filtering algorithm and PCNN algorithm respectively.It can be seen that using adaptive way to optimize network parameters can explore the maximum potential of PCNN as much as possible.

关 键 词：图像去噪 遗传蚁群算法 脉冲耦合神经网络 中值滤波 优化参数 

分 类 号：TP389[自动化与计算机技术—计算机系统结构]