[1]宋发兴,高留洋,刘东升,等.基于粒子群优化的BP神经网络图像复原方法[J].计算机技术与发展,2014,24(06):149-152.
　SONG Fa-xing,GAO Liu-yang,LIU Dong-sheng,et al.A Method of Image Restoration Based on Particle Swarm Optimization for BP Neural Network[J].,2014,24(06):149-152.
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基于粒子群优化的BP神经网络图像复原方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年06期
页码:
149-152
栏目:
智能、算法、系统工程
出版日期:
2014-06-30

文章信息/Info

Title:
A Method of Image Restoration Based on Particle Swarm Optimization for BP Neural Network
文章编号:
1673-629X（2014）06-0149-04
作者:
宋发兴; 高留洋; 刘东升; 米兰; 刘力维
中国洛阳电子装备试验中心
Author(s):
SONG Fa-xing; GAO Liu-yang; LIU Dong-sheng; MI Lan; LIU Li-wei
关键词:
BP神经网络; 粒子群优化; 图像复原
Keywords:
BP neural network; particle swarm optimization; image restoration
分类号:
TN911.73
文献标志码:
A
摘要:
针对BP神经网络易陷入局部最小、收敛速度慢的问题，研究了基于粒子群优化的学习算法，给出了具体的算法方案设计，并将其应用于图像复原。首先用高斯噪声对无噪图像进行模糊处理；然后将结果和原图像组成训练对，用于训练优化后的神经网络；最后利用训练好的神经网络对测试图像进行复原，从而达到去除噪声的目的。仿真结果表明，与BP神经网络相比， PSO-BP算法收敛速度快，迭代次数少，复原的图像在归一化均方误差(NMSE)和峰值信噪比(PSNR)的效果更好。
Abstract:
Aiming at the problem of local minimum,slow convergence of the BP neural network,the learning algorithm based on particle swarm optimization is designed and analyzed,which is applied to image restoration. Firstly,noiseless images are processed by Gaussian noise. Then,the result image and the noiseless images are made training pair,which is used in training the optimized BP neural network. Lastly,use the BP neural network to restore test images for the purpose of removing noise. The simulation results show that the effect of PSO-BP algorithm to recover the image have fast convergence rate and less iterations,is better than the BP neural network both in Nor-malized Mean Square Error ( NMSE) and the Peak Signal to Noise Ratio ( PSNR) .

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更新日期/Last Update: 1900-01-01