灰狼算法优化BP神经网络的图像去模糊复原

王海峰; 李萍; 王博; 翟帅华; 蔡楠

doi:10.3788/YJYXS20193410.0992

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灰狼算法优化BP神经网络的图像去模糊复原

图像处理 | 更新时间：2020-08-12

- 灰狼算法优化BP神经网络的图像去模糊复原
- Image deblurring restoration of BP neural networkbased on grey wolf algorithm
- 液晶与显示 2019年34卷第10期页码：992-999
- 作者机构：
  
  宁夏大学物理与电子电气工程学院, 宁夏银川 750021
- 作者简介：
  
  [ "王海峰(1991—), 男, 山东菏泽人, 硕士研究生, 2016年于青岛农业大学获得学士学位, 研究方向为模式识别与图像处理。E-mail:354993057@qq.com" ]
  [ "李萍(1962—), 女, 宁夏银川人, 硕士研究生导师, 教授, 1993年于大连海事大学获得硕士学位, 主要研究领域为电力系统及计算机技术。E-mail:lp_914@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61403219);2017年宁夏高校优秀青年培育基金项目(NGY2017009);National Natural Science Foundation of China(61403219);The Outstanding Youth Foundation Project of Ningxia Universities in 2017(NGY2017009)
- DOI：10.3788/YJYXS20193410.0992
  中图分类号： TP391.4
- 收稿日期：2019-04-16，
  
  录用日期：2019-5-10，
  
  纸质出版日期：2019-10-05
- 稿件说明：
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王海峰, 李萍, 王博, 等. 灰狼算法优化BP神经网络的图像去模糊复原[J]. 液晶与显示, 2019,34(10):992-999.

Hai-feng WANG, Ping LI, Bo WANG, et al. Image deblurring restoration of BP neural networkbased on grey wolf algorithm[J]. Chinese journal of liquid crystals and displays, 2019, 34(10): 992-999.
王海峰, 李萍, 王博, 等. 灰狼算法优化BP神经网络的图像去模糊复原[J]. 液晶与显示, 2019,34(10):992-999. DOI： 10.3788/YJYXS20193410.0992.

Hai-feng WANG, Ping LI, Bo WANG, et al. Image deblurring restoration of BP neural networkbased on grey wolf algorithm[J]. Chinese journal of liquid crystals and displays, 2019, 34(10): 992-999. DOI： 10.3788/YJYXS20193410.0992.

摘要

为了解决传统复原算法在退化图像复原过程中过度依赖先验知识弊端，提出利用BP神经网络学习和泛化能力强的优点进行退化图像复原研究。首先，采用BP神经网络进行退化图像的复原研究。然后，针对BP神经网络在学习过程中由于对网络初始值的过度依赖导致网络收敛速度慢、易于陷入局部极小值的缺点。提出利用灰狼优化算法的全局搜索能力对BP神经网络的初始参数进行优化，并利用改进收敛因子与动态权重指导种群移动的方式对灰狼算法进行改进。实验表明，本文提出的改进灰狼算法优化BP神经网络复原方法与维纳滤波算法、L-R复原算法、BP神经网络和PSO-BP神经网络等复原方式相比，收敛速度和复原精度方面得到大幅度提高，在客观的评价标准结构相似度与峰值信噪比方面都获得较好的数值结果。

Abstract

In order to solve the drawbacks of traditional restoration algorithms relying on a priori knowledge in the process of degraded image restoration

the advantages of BP neural network learning and generalization ability is proposed to carry out degraded image restoration research. Firstly

the BP neural network is used to recover the degraded image. Then

for the BP neural network

due to the excessive dependence on the initial value of the network

the network convergence speed is slow and it is easy to fall into the local minimum value. The global search ability of the grey wolf optimization algorithm is used to optimize the initial parameters of the BP neural network

and the gray wolf algorithm is improved by improving the convergence factor and dynamic weight to guide the population movement. The experiment shows that the improved grey wolf algorithm optimization BP neural network restoration method proposed in this paper has a high convergence speed and recovery accuracy compared with the Wiener filtering algorithm

LR restoration algorithm

BP neural network and PSO-BP neural network. It has good results in the objective evaluation standard structure similarity and peak signal to noise ratio.

关键词

Keywords

references

HELSTROM C W. Image restoration by the method of least squares[J]. Journal of the Optical Society of America , 1967, 57(3): 297-303.

徐大宏.基于正则化方法的图像复原算法研究[D].长沙: 国防科学技术大学, 2009.

XU D H. Research of image restoration algorithm based on regularization[D]. Changsha: National University of Defense Technology, 2009. (in Chinese)

刘小芳, 秦丽娟, 李莹.模糊图像迭代盲反卷积复原[J].沈阳理工大学学报, 2015, 34(5):28-32, 39.

LIU X F, QIN L J, LI Y. Blurred image restored by iterative blind deconvolution[J]. Journal of Shenyang Ligong University , 2015, 34(5): 28-32, 39. (in Chinese)

HUNT B R. The application of constrained least squares estimation to image restoration by digital computer[J]. IEEE Transactions on Computers , 1973, C-22(9): 805-812..

ZHOU Y T, CHELLAPPA R, VAID A, et al . Image restoration using a neural network[J]. IEEE Transactions on Acoustics , Speech , and Signal Processing , 1988, 36(7): 1141-1151.

李伟, 王正勇, 余艳梅.结合优化算法的神经网络图像复原算法[J].四川大学学报:自然科学版, 2011, 48(5):1081-1086.

LI W, WANG Z Y, YU Y M. Image restoration method based on neural network combining with optimization algorithms[J]. Journal of Sichuan University ( Natural Science Edition ), 2011, 48(5): 1081-1086. (in Chinese)

董俊.基于BP神经网络的图像复原算法研究[D].杨凌: 西安科技大学, 2009.

DONG J. Research on image restoration algorithm based on BP network[D]. Yangling: Xi'an University of Science and Technology, 2009. (in Chinese)

吴章平.粒子群算法在模糊图像复原中的应用研究[D].贵阳: 贵州大学, 2016.

WU Z P. Application of particle swarm optimization in fuzzy image restoration[D]. Guiyang: Guizhou University, 2016. (in Chinese)

孙胜永, 胡双演, 李钊, 等.基于粒子群优化算法的BP神经网络图像复原[J].无线电工程, 2014, 44(10):5-7, 26.

SUN S Y, HU S Y, LI Z, et al . BP neural network for image restoration based on particle swarm optimization[J]. Radio Engineering , 2014, 44(10): 5-7, 26. (in Chinese)

李扬, 吴敏渊, 邹炼.基于布谷鸟算法的BP神经网络图像复原[J].计算机应用, 2017, 37(S1):173-175, 197.

LI Y, WU M Y, ZOU L. BP neural network for image restoration based on cuckoo algorithm[J]. Journal of Computer Applications , 2017, 37(S1): 173-175, 197. (in Chinese).

李龙, 魏靖, 黎灿兵, 等.基于人工神经网络的负荷模型预测[J].电工技术学报, 2015, 30(8):225-230.

LI L, WEI J, LI C B, et al . Prediction of load model based on artificial neural network[J]. Transactions of China Electrotechnical Society , 2015, 30(8): 225-230. (in Chinese)

MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software , 2014, 69: 46-61.

李强.基于灰狼优化的特征选择方法研究[D].温州: 温州大学, 2017.

LI Q. Feature selection methods based on grey wolf optimization[D]. Wenzhou: Wenzhou University, 2017. (in Chinese).

樊纪香, 张宏, 李辉, 等.BP网络和多元线性回归在产量预测中的应用[J].计算机工程与应用, 2007, 43(23):203-204.

FAN J X, ZHANG H, LI H, et al . Application of BP neural network and multi-variable linear regression in rate prediction[J]. Computer Engineering and Applications , 2007, 43(23): 203-204. (in Chinese)

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