基于头脑风暴优化算法的BP神经网络模糊图像复原

梁晓萍; 郭振军; 朱昌洪

doi:10.11999/JEIT190261

基于头脑风暴优化算法的BP神经网络模糊图像复原

doi: 10.11999/JEIT190261

桂林航天工业学院桂林 541004

基金项目: 2019年度广西高校中青年教师科研基础能力提升项目(2019KY0802)，桂林航天工业学院电子信息重点学科及物联网与大数据应用研究中心项目(KJPT201805)

详细信息

作者简介:
梁晓萍：女，1992年生，硕士，研究方向为计算机图像处理等

郭振军：男，1977年生，高级工程师，博士，研究方向为物联网及应用、无线传感网等相关技术

朱昌洪：男，1978年生，高级工程师，研究方向为物联网及应用、无线传感网等相关技术

通讯作者:
郭振军　zjguo666@126.com

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2019-04-17
- 修回日期: 2019-09-03
- 网络出版日期: 2019-09-12
- 刊出日期: 2019-12-01

BP Neural Network Fuzzy Image Restoration Basedon Brain Storming Optimization Algorithm

Guilin University of Aerospace Technology, Guilin 541004, China

Funds: 2019 Guangxi University Young and Middle-aged Teachers’ Basic Scientific Research Ability Improvement Project (2019KY0802), The Project of Guilin University of Aerospace Technology Electronic Information Key Discipline and Internet Of Things and Big Data Application Research Center (KJPT201805)

摘要

摘要: 该文提出一种基于头脑风暴智能优化算法的BP神经网络模糊图像复原方法(OBSO-BP)。该方法在聚类和变异两方面优化了头脑风暴智能算法，利用头脑风暴优化算法易于解决多峰高维函数问题的特点，自动搜寻BP神经网络更佳的初始权值和阈值，以减少BP网络对其初始权值和阈值的敏感性，避免网络陷入局部最优解，增加网络的收敛速度，减小网络误差，提高图像还原质量。该文采用20张不同的图像，对其模糊图像分别进行维纳滤波复原(Wiener)、基于头脑风暴算法的维纳滤波复原(Wiener-BSO)、BP神经网络复原以及基于头脑风暴算法的BP神经网络(BSO-BP)图像复原实验。实验结果表明，该方法能够取得更好的图像复原效果。
- 图像复原 /
- BP神经网络 /
- 头脑风暴算法
Abstract: A kind of restoration method of BP neural network fuzzy image based on Optimized Brain Storming intelligent Optimized(OBSO-BP) algorithm is proposed in this paper. With the method of brain storming intelligent optimized algorithm which is optimized in both clustering and variation, issues of multi-peak high-dimensional function is easily solved. This method optimizes brain storming intelligence algorithm from two aspects of clustering and mutation. This method makes use of the characteristics of brain storming optimization algorithm, which is easy to solve multi-peak and high-dimensional function problems, to automatically search for better initial weights and thresholds of BP neural network, thus reducing the sensitivity of BP network to its initial weights and thresholds, avoiding the network falling into local optimal solution, increasing the convergence speed of the network and reducing the network error and improving the quality of image restoration. Twenty different images are adopted to the image restoration experiment of their fuzzy images with Wiener filtering restoration(Wiener), Wiener filtering restoration based on optimized Brain Storming intelligent Optimized algorithm(Wiener-BSO), BP neural network restoration and BP neural network restoration based on optimized Brain Storming intelligent Optimized algorithm(BSO-BP). Results show that a better effect of image restoration can be achieved with this method.
- Image restoration /
- BP neural network /
- Brain storming algorithm

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图 1 图像退化模型

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图 2 BP神经网络结构

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图 3 OBSO-BP图像复原流程图

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图 4 不同隐含层节点数的BP网络还原实验

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图 5 BSO-BP训练样本

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图 6 不同图像还原对比实验

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图 7 不同方法图像还原PSNR

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图 8 OBSO-BP和BSO-BP收敛曲线

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图 9 不同模糊参数OBSO-BP算法复原PSNR

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图 10 OBSO-BP抗噪性能测试结果

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参考文献(16)

刘海波, 杨杰, 吴正平, 等. 基于区间估计的单幅图像快速去雾[J]. 电子与信息学报, 2016, 38(2): 381–388. doi: 10.11999/JEIT150403

LIU Haibo, YANG Jie, WU Zhengping, et al. Fast single image dehazing based on interval estimation[J]. Journal of Electronics &Information Technology, 2016, 38(2): 381–388. doi: 10.11999/JEIT150403

梁晓萍, 罗晓曙. 基于遗传自适应的维纳滤波图像去模糊算法[J]. 广西师范大学学报: 自然科学版, 2017, 35(4): 17–23.

LIANG Xiaoping and LUO Xiaoshu. The adaptive wiener filtering deblurring based on the genetic algorithm[J]. Journal of Guangxi Normal University:Natural Science Edition, 2017, 35(4): 17–23.

何人杰, 樊养余, WANG Zhiyong, 等. 基于非局部全变分正则化优化的单幅雾天图像恢复新方法[J]. 电子与信息学报, 2016, 38(10): 2509–2514. doi: 10.11999/JEIT160208

HE Renjie, FAN Yangyu, WANG Zhiyong, et al. Novel single hazy image restoration method based on nonlocal total variation regularization optimization[J]. Journal of Electronics &Information Technology, 2016, 38(10): 2509–2514. doi: 10.11999/JEIT160208

杨爱萍, 郑佳, 王建, 等. 基于颜色失真去除与暗通道先验的水下图像复原[J]. 电子与信息学报, 2015, 37(11): 2541–2547. doi: 10.11999/JEIT150483

YANG Aiping, ZHENG Jia, WANG Jian, et al. Underwater image restoration based on color cast removal and dark channel prior[J]. Journal of Electronics &Information Technology, 2015, 37(11): 2541–2547. doi: 10.11999/JEIT150483

沈峘, 李舜酩, 毛建国, 等. 数字图像复原技术综述[J]. 中国图象图形学报, 2009, 14(9): 1764–1775. doi: 10.11834/jig.20090909

SHEN Huan, LI Shunming, MAO Jianguo, et al. Digital image restoration techniques: A review[J]. Journal of Image and Graphics, 2009, 14(9): 1764–1775. doi: 10.11834/jig.20090909

HE Fei and ZHANG Lingying. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network[J]. Journal of Process Control, 2018, 66: 51–58. doi: 10.1016/j.jprocont.2018.03.005

LIANG Yueji, REN Chao, WANG Haoyu, et al. Research on soil moisture inversion method based on GA-BP neural network model[J]. International Journal of Remote Sensing, 2019, 40(5/6): 2087–2103. doi: 10.1080/01431161.2018.1484961

CHEN Yegang. Prediction algorithm of PM2.5 mass concentration based on adaptive BP neural network[J]. Computing, 2018, 100(8): 825–838. doi: 10.1007/s00607-018-0628-3

李青峰, 胡访宇. 利用BP神经网络实现监控图像盲复原[J]. 计算机仿真, 2009, 26(5): 223–226. doi: 10.3969/j.issn.1006-9348.2009.05.058

LI Qingfeng and HU Fangyu. Blind restoration of monitoring image based on BP neural network[J]. Computer Simulation, 2009, 26(5): 223–226. doi: 10.3969/j.issn.1006-9348.2009.05.058

赵秀谊. 头脑风暴优化算法及其应用研究[D]. [硕士论文], 西安理工大学, 2013.

ZHAO Xiuyi. Research and application of brain storm optimization algorithm[D]. [Master dissertation], Xi’an University of Technology, 2013.

SHI Yuhui. Brain Storm Optimization Algorithm[M]. Advances in Swarm Intelligence. Berlin Heidelberg: Springer, 2011: 303–309. doi: 10.1007/978-3-642-21515-5_36.

周恒俊. 智能优化算法及其在图像检索中的应用研究[D]. [硕士论文], 山东大学, 2016.

ZHOU Hengjun. Research on intelligent optimization algorithm and its application in image retrieval[D]. [Master dissertation], Shandong University, 2016.

陈宏伟, 鄢来仪, 叶志伟. 头脑风暴算法在多阈值Otsu分割法中的应用[J]. 湖北工业大学学报, 2017, 32(5): 59–62. doi: 10.3969/j.issn.1003-4684.2017.05.016

CHEN Hongwei, YAN Laiyi, and YE Zhiwei. Application of brain storm optimization algorithm in multilevel threshold Otsu segmentation[J]. Journal of Hubei University of Technology, 2017, 32(5): 59–62. doi: 10.3969/j.issn.1003-4684.2017.05.016

梁志刚, 顾军华. 改进头脑风暴优化算法与Powell算法结合的医学图像配准[J]. 计算机应用, 2018, 38(9): 2683–2688. doi: 10.11772/j.issn.1001-9081.2018020353

LIANG Zhigang and GU Junhua. Medical image registration by integrating modified brain storm optimization algorithm and Powell algorithm[J]. Journal of Computer Applications, 2018, 38(9): 2683–2688. doi: 10.11772/j.issn.1001-9081.2018020353

HECHT-NIELSEN R. Theory of the backpropagation neural network[C]. International 1989 Joint Conference on Neural Networks, Washington, USA, 1989: 593–605. doi: 10.1109/IJCNN.1989.118638.

ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]. The 2nd International Conference on Knowledge Discovery and Data Mining, Portland, USA, 1996: 226–231.

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