基于L-曲率流滤波器的图像降噪算法

周昌雄; 于盛林; 祖克举

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基于L-曲率流滤波器的图像降噪算法

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- 基于L-曲率流滤波器的图像降噪算法
- Image denoising algorithm based on L-curvature flow filter
- 光学精密工程 2005年13卷第6期页码：759-765
- 作者机构：
  
  1. 南京航空航天大学自动化学院,江苏南京,210016
  2. 江苏科技大学电子信息学院,江苏镇江,212003
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TP391
- 收稿日期：2005-04-12，
  
  修回日期：2005-08-18，
  
  网络出版日期：2005-12-30，
  
  纸质出版日期：2005-12-30
- 稿件说明：
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周昌雄, 于盛林, 祖克举. 基于L-曲率流滤波器的图像降噪算法[J]. 光学精密工程, 2005,13(6):759-765.

ZHOU Chang-xiong, YU Sheng-lin, ZU Ke-ju. Image denoising algorithm based on L-curvature flow filter[J]. Optics and precision engineering, 2005, 13(6): 759-765.
周昌雄, 于盛林, 祖克举. 基于L-曲率流滤波器的图像降噪算法[J]. 光学精密工程, 2005,13(6):759-765. DOI：

ZHOU Chang-xiong, YU Sheng-lin, ZU Ke-ju. Image denoising algorithm based on L-curvature flow filter[J]. Optics and precision engineering, 2005, 13(6): 759-765. DOI：

摘要

提出了L-曲率流滤波器的图像降噪(滤波)算法

该方法按图像信噪比大小分高、中、低3类

分别由L滤波器降噪、多级L滤波器降噪以及多次迭代的组合滤波器降噪

并进行了实验研究。结果表明:该算法与均值和中值滤波器相比

输入图像信噪比越低

滤波效果越明显。当输入图像为低信噪比时

对于受高斯噪声污染的图像

该算法滤波比均值滤波平均提高2.98 dB;对于受脉冲噪声污染的图像

该算法滤波比中值滤波平均提高11.09 dB

说明该算法对降低不同种类和不同信噪比的图像噪声有较强的适应性。

Abstract

Denoising algorithm of L-curvature flow filter was presented. The image noise was obviously removed according to SNR level in terms of the algorithm. L filter

multistage L filter

and the filter combined L filter with curvature flow filter through many iterations

could filter image noise of higher

middle

and lower SNR level respectively. Experiment results show that the lower the input image SNR is

the better the performance of developed algorithm is comparing with the average filter and mean filter. When input image SNR level is low

output image SNR of the algorithm is about 2.98 dB higher than average filter for images with Gaussian noise

and 11.09 dB higher than mean filter for images with impulse noise. The method is very efficient to decrease the image noise of different kinds of SNR and intensities.

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Keywords

references

. PITAS I,VENERSANOPOULUS A N.Nonlinear digital filter:principle and applications[M]. Kluwer, Dordrecht,1990.

. 杨新.图象偏微分方程的原理与应用[M]. 上海:上海交通大学出版社,2003. YANG X. Partial differentiation equation principle and application [M]. Shanghai: Shanghai Jiaotong University Press,2003.(in Chinese)

. 佟首峰,阮锦,郝志航. CCD传感器降噪技术的研究 . 光学精密工程,2000,8(2):140-145. TONG SH F, RUAN J, HAO ZH H. Noise reducing scheme on output signal of CCD[J]. Optics and Precision Engineering, 2000,8(2):140-145.(in Chinese)

. 孙俊喜, 陈亚珠. 一种具有边缘保持特性的超声图像小波域值去噪新方法[J]. 光学精密工程,2002,10(5):429-433. SUN J X, CHEN Y ZH. Novel speckle reduction for medical ultrasound images based on edge preservation[J]. Optics and Precision Engineering, 2002,10(5):429-433.(in Chinese)

. 王全明,张雄伟. 一种基于自适应模糊滤波的语音增强方法[J]. 解放军理工大学学报(自然科学版),2003,4(1):17-20. WANG Q M, ZHANG X W. A new method of speech enhancement based on adaptive fuzzy filter[J].Journal of PLA University of Science and Technology(Natural Science), 2003,4(1):17-20.(in Chinese)

. 刘忠仁孙圣和. 基于模糊神经网络的脉冲噪声滤波器[J]. 中国图象图形学报,2001,6A(4):344-347. LIU ZH R, SUN SH H. Impulse noise filter based on fuzzy neural network[J]. Journal of Image and Graphics, 2001,6A(4):344-347.(in Chinese)

. 黄永红. 一种细胞神经网络图像滤波方法[J]. 江苏理工大学学报(自然科学版),2000,21(1):83-86. HUANG Y H. An approach for image processing by cellular neural network . Journal of Jiangsu University of Science and Technology (Natural Science), 2000,21(1):83-86.(in Chinese)

. 史彩成,赵保军,韩月秋,等. 基于模糊理论的红外图像滤波[J]. 激光与红外,2001,31(2):113-114. SHI C CH,ZHAO B J, HAN Y Q,et al. IR image filtering based on fuzzy[J]. Laser & Infrared, 2001,31(2):113-114.(in Chinese)

. 陈贺新. 非线性滤波器与数字图象处理[M]. 北京:国防工业出版社,1997. CHEN H X. Nonlinear filter and digital image processing [M]. Beijing: National Defense Industry Press,1997.(in Chinese)

. 郭炳庆,卢强,黎雄. 一种基于局部统计量的自适应L滤波器[J]. 电子学报,2001,29(7):909-911. GUO B Q, LU Q, LI X. An adaptive L-filter based on signal local statistics[J]. Acta Electronica Sinica, 2001,29(7):909-911.(in Chinese)

. MALLADI R,SETHIAN J A. Image processing: flows under min/max curvature and mean curvature[J]. Graphical Models and Image Processing,1996,58(2):12-141.

. SETHIAN J A. Level set methods and fast marching methods: evolving interface in computational geometry, fluid mechanics, computer vision, and materials science[M]. Cambridge University Press, 1999.

. 李江,程键,周鑫. 数字图像处理中多窗口下的自适应中值滤波[J]. 计算机工程, 2003,29(17):154-156. LI J, CHENG J, ZHOU X. Self-adapting median filtering under multi-window structures in the digital image processing[J]. Computer Engineering, 2003,29(17):154-156.(in Chinese)

. 林晓梅,李琳娜,牛刚,等. 基于小波边缘检测的图像去噪方法[J]. 光学精密工程, 2004,12(1):88-93. LIN X M, LI L N, NIU G, et al. Image denoising based on wavelet edge detection[J]. Optics and Precision Engineering, 2004,12(1):88-93.(in Chinese)

. 勒中鑫. 数字图象信息处理[M]. 北京: 国防工业出版社.2003. LE ZH X. Digital image information processing[M]. Beijing: National Defense Industry Press,2003.(in Chinese)

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