计算机应用 ›› 2012, Vol. 32 ›› Issue (03): 729-731.DOI: 10.3724/SP.J.1087.2012.00729

• 图形图像技术 • 上一篇    下一篇

灰度极限脉冲耦合神经网络混合噪声滤波

程园园1,李海燕1,陈海涛2,施心陵1   

  1. 1.云南大学 信息学院,昆明 650091;
    2.昆明医学院第三附属医院 超声科,昆明 650118
  • 收稿日期:2011-09-19 修回日期:2011-12-05 发布日期:2012-03-01 出版日期:2012-03-01
  • 通讯作者: 李海燕
  • 作者简介:程园园(1985-),女,山东荷泽人,硕士研究生,主要研究方向:人工神经网络图像去噪;李海燕(1976-),女,云南红河人,副教授,博士,主要研究方向:人工神经网络图像去噪、特征提取;陈海涛(1980-),男,陕西扶风人,医师,硕士,主要研究方向:超声医学诊断与介入治疗;施心陵(1956-),男,云南昆明人,教授,博士生导师,主要研究方向:智能信号检测与处理。
  • 基金资助:

    云南大学第二批中青年骨干教师基金及在职培养博士启动基金资助项目(21132014);第三届云南大学研究生科研课题资助基金资助项目(YNUY201046)。

Mixed noise filtering via limited grayscale pulse coupled neural network

CHENG Yuan-yuan1, LI Hai-yan1, CHEN Hai-tao2, SHI Xin-ling1   

  1. 1.School of Information Science and Engineering, Yunnan University, Kunming Yunnan 650091, China;
    2.Ultrasound Department, The Third Affiliated Hospital, Medical University of Yunnan, Kunming Yunnan 650118, China
  • Received:2011-09-19 Revised:2011-12-05 Online:2012-03-01 Published:2012-03-01
  • Contact: Hai-yan LI

摘要: 针对图像中同时存在椒盐噪声和高斯噪声,提出一种基于灰度极限和脉冲耦合神经网络(PCNN)滤除混合噪声的新方法。首先,根据灰度极值定位出椒盐噪声点;其次,在滤波窗口中对椒盐噪声点进行均值滤波;然后,利用PCNN赋时矩阵定位出高斯噪声点;最后,自适应调整可变灰度步长,选择不同滤波方法滤除高斯噪声。实验结果表明提出的算法较常见的混合噪声滤波方法在主观滤波效果和客观评价指标峰值信噪比(PSNR)及信噪比改善因子(ISNR)两方面均有明显的优势。

关键词: 椒盐噪声, 高斯噪声, 灰度极限, 脉冲耦合神经网络, 均值滤波, 可变步长

Abstract: A new method of filtering mixed noise based on limited grayscale and Pulse Coupled Neural Network (PCNN) was proposed for an image contaminated by salt and pepper noise and Gaussian noise. First, salt and pepper noise was identified according to the limited grayscale in a detecting window. Then the noise was filtered via mean filter in a filtering window. Subsequently, Gaussian noise was identified by using the time matrix of PCNN. Finally the Gaussian noise was filtered by some different filters based on variable step. The experimental results show that the proposed method has more advantages not only in filtering effects but also in objective evaluation indexes of Peak Signal-to-Noise Ratio (PSNR) and Improved Signal-to-Noise Ratio (ISNR) compared to some traditional methods.

Key words: salt and pepper noise, Gaussian noise, limited grayscale, Pulse Coupled Neural Network (PCNN), mean filtering, variable step

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