期刊文献+

基于小波分析的CT图像噪声类型识别 被引量:4

Identifying of Noise Types for Computed Tomography Images Based on Wavelet Analysis
下载PDF
导出
摘要 对CT图像噪声的类型进行识别,采用相适应的去噪方法提高图像去噪效果,减少去噪中的盲目性。分析小波高频子带系数的能量分布,利用直方图的信噪比和曲线拟合图的积分,对CT图像中最常见的两类噪声,即高斯噪声和椒盐噪声进行识别。直方图的信噪比R为0.2,曲线拟合图积分A为60,可作为高斯噪声和椒盐噪声分界线。对大量含噪CT图像的实验结果表明,该方法对CT图像噪声类型的识别比较准确。 To identify the noise types of CT image,corresponding method was used to improve the effect of image denoising,reduce the blindness.By analysis the energy distribution of the HH subband's coefficients in the wavelet domain,SNR of the histogram and area of curve fitting was used to distinguish Gaussian white noise and saltpepper noise in computed tomography images.We found histogram of the signal to noise ratio R equals 0.2,curve fitting map area A equals 60 are the distinguish lines between Gaussian white noise and saltpepper noise.The experiments on a wide variety of images show the veracity of this method in distinguishing noise.
出处 《CT理论与应用研究(中英文)》 2011年第2期183-190,共8页 Computerized Tomography Theory and Applications
基金 广州科技支撑重点项目(2009Z1-E341)
关键词 小波分析 噪声类型 信噪比 面积 wavelet analysis noise types SNR area
  • 相关文献

参考文献11

  • 1Xie JC, Zhang DL, Wu WL. Overview on wavelet image denoising[J]. Journal of Image and Graphics, 2002, 7A($): 209-217.
  • 2Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling & Simulation, 2005, 4(2): 490-530.
  • 3Gonzalez RC, Woods RE. Digital image processing[M]. 2 ed. Beijing: Publishing House of Electron Industry, 2002. 88-94.
  • 4~iller M, Kingsbury N. Image denoising using derotated complex wavelet coefficients[J]. IEEE Transactions on Image Processing, 2008, 17: 1500-1511.
  • 5张旗,梁德群,樊鑫,李文举.基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报,2004,23(4):281-285. 被引量:32
  • 6徐莉,黄地龙,赵宁.基于小波分析的自适应噪声识别[J].铁路计算机应用,2007,16(8):11-14. 被引量:2
  • 7Oonoho DL, Johnstone IM. Ideal spatial adaptation via wavelet shrinkage[J]. Biometrika, 1994, 81: 425-455.
  • 8Zhang Z, Blum RS. On estimating the quality of noisy images[A]. IEEE International Conference on Acoustic Speech and Signal Processing, 5, 1998: 2897-2900.
  • 9Bui TD, Chen GY. Translation invariant denoising using multiwavelets[J], IEEE Transactions on Signal Processing, 1998, 46(12): 3414-3420.
  • 10Chen GY, Bui TD. Multiwavelet denoising using neighboring coefficients[J]. IEEE Signal Processing Letters, 2003, 10(7) : 211-214.

二级参考文献7

  • 1张旗,梁德群,樊鑫,李文举.基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报,2004,23(4):281-285. 被引量:32
  • 2[2]Rafael C Gonzalez, Richard E Woods. Digital Image Processing(Second Edition)[M].Beijing: Publishing House of Electron Industry (数字图像处理. 北京:电子工业出版社), 2002.
  • 3[3]Chang S Grace, Bin Yu, Martin Vetterli. Spatially adaptive wavelet thresholding with context modeling for image denoising[J]. IEEE Trans. on Image Processing, 2000, 9(9): 1522-1531.
  • 4[4]Meer P, Jolion J, Rosenfeld A. A fast parallel algorithm for blind estimation of noise variance[J]. IEEE Trans. on Pattern Algorithm and Machine Intelligence, 1990, 12(2): 216-223.
  • 5[5]Zhang Z, BlumRick S. On estimating the quality of noisy images[A]. IEEE International Conference on Acoustic Speech and Signal Processing, 1998, 5: 2897-2900.
  • 6[6]Donoho D L, Johnstone I M. Ideal spatial adaptaition via wavelet shrinkage[J]. Biometrika, 1994, 81: 425-455.
  • 7谢杰成,张大力,徐文立.小波图象去噪综述[J].中国图象图形学报(A辑),2002,7(3):209-217. 被引量:254

共引文献31

同被引文献52

引证文献4

二级引证文献24

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部