期刊文献+

基于小波分析的阈值去噪改进算法 被引量:6

Improving threshold algorithm deleting noise based on wavelet analysis
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摘要 针对小波边缘检测阈值设定问题,本文提出了一种基于小波分析的改进阈值设定方法,采用一个矫正因子β来构造一个新的阙值函数,调整软硬阚值的恒定偏差。实验证明,新阈值函数不但整体上连续性好而且在克服硬阙值函数的不连续以及软阈值函数在处理较大小波系数时总存在恒定偏差这2个不足的同时,又保留了软、硬阈值函数原有的优点。 As for how to choose a proper threshold in the edge detection, a method of improving threshold based on wavelet analysis was proposed in this paper. We reconstructed a new threshold function through an adaptive parameter β to adjust the invariableness windage of the soft-threshold and hard-threshold. Experiment results show that the new threshold function not only can be continuous, but also can preserve the advantage of the soft-threshold and hard-threshold.
出处 《国外电子测量技术》 2007年第3期12-13,共2页 Foreign Electronic Measurement Technology
关键词 小波分析 软阈值 硬阈值 去噪 wavelet analysis soft-threshold hard-threshold
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参考文献6

  • 1邓自立.解耦Wiener状态滤波器.中国学术期刊文摘,2000,6(8):979-980.
  • 2CHUI C K,CHEN G.Kalman Filtering with real time applications[J].Springer-Verlag,1987:125-136.
  • 3DOHONO D L.De-noising by soft-thresholding[J].IEEE Trans Inform Theory,1995,41(3):613.
  • 4SAGE A P,HUSA G W.Adaptive filtering with unknown prior statistics[A].Joint Automation Control Conference[C].1996:760-790.
  • 5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:252
  • 6费佩燕,郭宝龙,章正宇.基于二进小波变换的图像去噪技术研究[J].西安电子科技大学学报,2003,30(4):492-496. 被引量:13

二级参考文献76

  • 1郭宝龙,郭雷.用扩散和集中神经网络区分图形与背景[J].科学通报,1994,39(19):1805-1808. 被引量:5
  • 2郭宝龙,郭雷.视觉运动计算的新方法[J].西安电子科技大学学报,1994,21(4):457-463. 被引量:11
  • 3[9]You Yuli, Kaveh D. Fourth-order partial differential equations for noise removal[J]. IEEE Trans. Image Processing, 2000,9(10):1723~1730.
  • 4[10]Bouman C, Sauer K. A generalized Gaussian image model of edge preserving map estimation[J]. IEEE Trans. Image Processing, 1993,2(3):296~310.
  • 5[11]Ching P C, So H C, Wu S Q. On wavelet denoising and its applications to time delay estimation[J]. IEEE Trans. Signal Processing,1999,47(10):2879~2882.
  • 6[12]Deng Liping, Harris J G. Wavelet denoising of chirp-like signals in the Fourier domain[A]. In:Proceedings of the IEEE International Symposium on Circuits and Systems[C]. Orlando USA, 1999:Ⅲ-540-Ⅲ-543.
  • 7[13]Gunawan D. Denoising images using wavelet transform[A]. In:Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing[C]. Victoria BC,USA, 1999:83~85.
  • 8[14]Baraniuk R G. Wavelet soft-thresholding of time-frequency representations[A]. In:Proceedings of IEEE International Conference on Image Processing[C]. Texas USA,1994:71~74.
  • 9[15]Lun D P K, Hsung T C. Image denoising using wavelet transform modulus sum[A]. In:Proceedings of the 4th International Conference on Signal Processing[C]. Beijing China,1998:1113~1116.
  • 10[16]Hsung T C, Chan T C L, Lun D P K et al. Embedded singularity detection zerotree wavelet coding[A].In:Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan, 1999:274~278.

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