基于小波域的图像椒盐噪声密度估计

李天翼; 王明辉; 林涛; 余文森

基于小波域的图像椒盐噪声密度估计

四川大学计算机学院, 成都 610065

详细信息

中图分类号: TP 391
计量
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- 被引次数: 0
出版历程
- 收稿日期: 2010-11-08
- 网络出版日期: 2012-02-29

Estimation of salt-pepper noise in images in wavelet domain

College of Computer Science, Sichuan University, Chengdu 610065, China

摘要

摘要: 提出一种基于小波域的椒盐噪声密度估计方法.利用图像信号在小波域的系数具有稳定近似的分布,以及噪声对小波系数影响的特点,定量地分析了含噪图像的系数幅值直方图与原始图像的系数幅值直方图之间的偏离程度随噪声密度的变化规律,揭示这种变化关系对图像具有强的鲁棒性,从而利用这种关系对噪声进行估计.仿真结果表明,相对于目前方法,提出算法性能更佳,能够获得更准确的估计值和更小的估计偏差.
- 小波系数 /
- 相关系数 /
- 直方图 /
- 椒盐噪声 /
- 密度估计
Abstract: A novel approach was proposed for estimating the density of salt-pepper noise in images using wavelet transform. On the basis of the fact that the wavelet coefficients of all natural images conform to stable and close distribution, as well as such distribution of the noisy image may be influenced by the noise, the proposed algorithm exhibits how the wavelet coefficients magnitude histogram of the noisy image deviates from that of original image along with the density of the salt-pepper noise in quantitative form, and indicates that the degree of such deviation is nearly determined by the noise density, i.e., the change relation is robust to image traits. The proposed algorithm thus takes advantage of this relation to make estimation. Compared with those of existing methods, simulation results show that the proposed approach has more exact estimation value and less deviation.
- wavelet coefficient /
- correlation coefficient /
- histogram /
- salt-pepper noise /
- density estimation

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参考文献(15)

[1]	Gallagher Jr N C,Wise G L.A theoretical analysis of properties of the median filters[J].IEEE Trans on Acoustics Speech,Signal Processing,1981,29(1):1136-1141
[2]	Hwang H,Haddad R A.Adaptive median filters:new algorithms and results[J].IEEE Transactions on Image Processing,1995,4(4):499-502
[3]	Xu H X,Zhu G X,Peng H Y.Adaptive fuzzy switching filter for images corrupted by impulse noise[J].Pattern Recognition Letters,2004,25(15):1657-1663
[4]	Qin Peng,Ding Runtao.Ordering threshold switching median filter[J].Journal of Image and Graphics,2004,9(4):412-416
[5]	Wang Z,Zhang D.Progressive switching median filter for the removal of impulse noise from highly corrupted images[J].IEEE Transactions on Circuits System,1999,46(1):78-80
[6]	Xing Z J,Wang S J,Deng Haojiang,et al.A new filtering algorithm based on extremum and median value[J].Journal of Image and Graphics,2001,6(6):533-536
[7]	Yang R K,Yin L,Gabbouj M,et al.Optimal weighted median filtering under structural constraints[J].IEEE Transactions on Signal Processing,1995,43(3):591-604
[8]	Wang Z,Zhang D.Restoration of impulse noise corrupted images using long-range correlation[J].IEEE Signal Processing Letters,1998,5(1):4-7
[9]	Song Y,Li M T,Sun L N.Image salt & pepper noise self-adaptive suppression algorithm based on similarity function[J].Acta Automatic Sinica,2007,33(5):474-479
[10]	Wang B,Pan Q.Soft-threshold histogram weighted filtering with correlativity for high density salt-pepper noise images[J].Acta Electronica Sinica,2007,35(7):1347-1351
[11]	Zhang Q,Liang D Q,Fan X.Identifying of nosie types and estimating of noise level for a noisy image in the wavelet domain[J].Journal of Infrared Millimeter Waves,2004,23(4):281-285
[12]	Chao Z H,Li Y J,Zhang K.Estimation of salt & pepper noise on the magnitude spectrum[J].Infrared Technology,2006,28(9):549-551
[13]	Mallat S G.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE Trans Pattern Analysis and Machine Intell,1989,11(7):674-693
[14]	Mallat S G.A wavelet tour of signal processing[M].2nd ed.Beijing:China Machine Press,2006
[15]	Gonzalez R C,Woods R E,Eddins S L.Digital image processing using matlab[M].Upper Saddle River,New Jersey:Prentice Hall,2005