wavelet adaptive denoising method based on PCNN

LI Yun-hong; YI Xin

doi:10.3788/OPE.20122009.2060

您当前的位置：

首页 >

文章列表页 >

wavelet adaptive denoising method based on PCNN

Article | 更新时间：2020-08-12

- wavelet adaptive denoising method based on PCNN
- Optics and Precision Engineering Vol. 20, Issue 9, Pages: 2060-2067(2012)
- 作者机构：
  
  西安工程大学电子信息学院,陕西西安,中国,710048
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122009.2060
  CLC： Q-334;TP391.4
- Received：23 May 2012，
  
  Revised：06 July 2012，
  
  Published：10 September 2012
- 稿件说明：
移动端阅览
李云红, 伊欣. 基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法[J]. 光学精密工程, 2012,20(9): 2060-2067

LI Yun-hong, YI Xin. wavelet adaptive denoising method based on PCNN[J]. Editorial Office of Optics and Precision Engineering, 2012,20(9): 2060-2067
李云红, 伊欣. 基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法[J]. 光学精密工程, 2012,20(9): 2060-2067 DOI： 10.3788/OPE.20122009.2060.

LI Yun-hong, YI Xin. wavelet adaptive denoising method based on PCNN[J]. Editorial Office of Optics and Precision Engineering, 2012,20(9): 2060-2067 DOI： 10.3788/OPE.20122009.2060.

摘要

分析了维纳滤波原理和脉冲耦合神经网络(PCNN)模型的特点

根据斑点噪声统计模型的特征

结合小波变换方法

提出了一种基于PCNN模型的小波自适应斑点噪声滤除算法(W-PCNN-WD)来改善超声图像质量。首先

对超声图像进行对数变换

使斑点噪声转换为加性噪声; 对医学图像进行维纳滤波处理

计算其加性噪声的标准方差

并以此作为小波阈值。然后

利用小波变换对图像进行预处理

利用PCNN在小波域中对小波系数进行相应的修正。最后

进行小波逆变换和指数变换

获得滤除噪声的图像。结果表明:本文提出的滤波方法优于其他滤波方法

当噪声方差为0.01时

本文滤波算法获得的峰值信噪比(PSNR)比经Wiener滤波方法获得的高出9 dB。该滤波方法能在有效去除超声斑点噪声的基础上保留图像的边缘细节信息

极大地改善了图像的视觉质量。

Abstract

The Wiener filtering principle and characteristics of a Pulse Couple Neural Network(PCNN) model were analyzed and a wavelet adaptive denoising method based on the PCNN(W-PCNN-WD)was proposed according to a statistical model of speckle noise combined with a wavelet transform to improve the quality of ultrasound image. Firstly

the ultrasound image was performed a log conversion to transform the speckle noise to an additive noise. Then

the Wiener filtering was used to process the medical image to get the variance of the additive noise as the threshold of wavelet. Furthermore

the image was preprocessed by the wavelet transform and wavelet coefficients were recomposed appropriately by using the PCNN. Finally

the image was processed again by the wavelet inverter and the exponential transforms to get a denoising image. The result shows that the proposed filtering method is better than the other filtering methods

and the Peak Signal to Noise Ratio( PSNR) from the proposed method is higher 9 dB than that from the Wiener filtering when the noise variance is 0.01. The method can keep the edge details of the information on the basis of removing speckle noise

which improves the visual quality of images greatly.

关键词

Keywords

references

姜玉新,王志刚. 医学超声影像学 [M].北京:人民卫生出版社,2010:1-10. JIANG Y X, WANG ZH G. Medical Ultrasound Imageology[M].Beijing:People's Health Publishing House,2010:1-10.(in Chinese)[2] 郭业才,王绍波.基于PCNN的小波域超声医学图像去噪方法[J]. 安徽大学学报:自然科学版,2010,34(5): 54-59. GUO Y C, WANG SH B. Method of medical ultrasonic image de-noising based on PCNN in the wavelet domain[J]. Journal of Anhui University:Natural Science Edition, 2010,34(5): 54-59. (in Chinese)[3] 陈韬亦. 医学超声图像去噪方法研究.哈尔滨:哈尔滨工业大学,2008. CHEN T Y. Research on medical ultrasound image denoising.Harbin:Harbin Institute of Technology,2008.(in Chinese)[4] 王绍波,郭业才,高敏,等.基于模糊PCNN的小波域超声医学图像去噪方法[J]. 光电子激光,2010, 21(3):476-480. WANG SH B, GUO Y C, GAO M, et al.. Method of medical ultrasonic image de-noising based on fuzzy PCNN in the wavelet domain[J]. Journal of OptoelectronicsLaser, 2010, 21(3):476-480.(in Chinese)[5] 倪林.小波变换与图像处理 [M].合肥:中国科学技术大学出版社,2010:140-146. NI L. Wavelet Transform and Image Processing[M].Hefei: University of Science and Technology of China Press,2010:140-146.(in Chinese)[6] 化莉.基于小波的图像去斑点噪声方法的研究[J]. 哈尔滨师范大学:自然科学学报,2010,26(3):37-40. HUA L. Research on the method of speckle noise based on wavelet image[J]. Natural Sciences Journal of Harbin Normal University, 2010,26(3):37-40.(in Chinese)[7] 张天瑜.基于维纳滤波的小波图像去噪算法研究[J]. 井冈山大学学报:自然科学版,2011,32(1):92-96. ZHANG T Y. Research on wavelet image denoising algorithm based on WIENER filter[J]. Journal of Jinggangshan University:Natural Science, 2011,32(1):92-96.(in Chinese)[8] 陈晓曦,王延杰,刘恋. 小波阈值去噪法的深入研究[J]. 激光与红外,2012,42(1):105-110. CHEN X X, WANG Y J, LIU L. Deep study on wavelet threshold method for image noise removing[J]. Laser＆Infrared, 2012,42(1):105-110.(in Chinese)[9] DONOHO D L. Denoising by soft-thresholding [J]. IEEE Trans on Information Theory, 1995,41(3):613-627.[10] 汪源源,焦静. 改进型脉冲耦合神经网络检测乳腺肿瘤超声突袭那个感兴趣区域[J]. 光学精密工程,2011,19(6):1398-1405. WANG Y Y, JIAO J. Detection of regions of interests from breast tumor ultrasound images using improved PCNN[J]. Opt. Precision Eng., 2011,19(6):1398-1405.(in Chinese)[11] 武治国,王延杰,李桂菊. 应用小波变换的自适应脉冲耦合神经网络在图像融合中的应用[J]. 光学精密工程,2010,18(3):708-715. WU ZH G, WANG Y J, LI G J. Application of adaptive PCNN based on wavelet transform to image fusion[J]. Opt. Precision Eng., 2010,18(3):708-715.(in Chinese)[12] ZHANG H J, ZHANG Z N,LIN D M,et al.. A novel image de-noising algorithm combined PCNN with morphology.2007 International Symposium on Intelligent Signal Processing and Communication Systems, 2007:281-284.[13] MA Y D,SHI F,LI L. A new kind of impulse noise filterbased on PCNN[J].IEEE ICNNSP,2003,1(1):152-155.[14] 马义德. 脉冲耦合神经网络与数字图像处理 [M].北京:科学出版社,2008:1-54. MA Y D. PCNN and Digital Image Processing[M].Beijing: Science Press,2008:1-54.(in Chinese)[15] KUNTIMAD G, RANGANATH H S. Perfect image segmentation using pulse coupled neural networks[J].IEEE Trans Neural Networks,1999, 10(3): 591-598.[16] JOHNSON J, PADGETT L. PCNN models and application[J]. IEEE Trans Neural Networks, 1999, 10(3): 480-498.[17] 陈兴杰,柴晓冬.一种基于简化PCNN的红外图像分割方法[J]. 安徽大学学报:自然科学版,2010,34(1):74-77. CHEN X J, CHA X D. Infrared image segmentation based on a simplified PCNN[J]. Journal of Anhui University:Natural Science Edition, 2010,34(1):74-77.(in Chinese)[18] 邢占峰.超声医学图像处理中若干问题的研究.天津:天津大学,2003. XING ZH F. Study on several problems of ultrasound medical image processing, Tianjing: Tianjin University, 2003. (in Chinese)

Views

192

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi-feature cross UAV image detection algorithm under cross-layer attentional interaction

Wavelet dehazeformer network for road traffic image dehazing method

Multi-scale decomposition of point cloud data based on wavelet transform

Coarse-to-fine underwater image enhancement based on multi-level wavelet transform

Feature point detection for optical and SAR remote sensing images registration

Related Author

ZHANG Zhihao

DU Lixia

HOU Yue

HAO Ziwei

YIN Jie

XIA Ping

LI Ziyi

LEI Bangjun

Related Institution

College of Electronic and Information Engineering， Lanzhou Jiaotong University

College of Computer and Information Technology， Three Gorges University

Hubei Key Laboratory of Intelligent Vision based Monitoring for Hydroelectric Engineering， Three Gorges University

Beijing Institute of Aerospace Metrology and Testing Technology

College of Mechanical & Electrical Engineering， Nanjing University of Aeronautics and Astronautics

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰