期刊文献+

基于小波变换的红外图像去噪 被引量:33

Infrared Image Denoising Based on Wavelet Transform
下载PDF
导出
摘要 提出一种基于新型阈值函数的小波域红外图像去噪法,其阈值函数表达式简单且连续,既克服了硬阈值函数不连续的缺点,又克服了软阈值函数中估计小波系数与含噪小波系数间存在恒定偏差的缺陷。同时新的阈值函数还有效地利用了小波系数的成串性,即在小波系数的估计计算中考虑了邻域小波系数的大小。仿真结果表明,在去噪红外图像视觉效果和峰值信噪比两个方面,文中提出的去噪法优于已有的各种门限去噪法和Matlab-wiener 2滤波算法。 Wavelet-domain infrared image denoising based on a new kind of thresholding function is proposed. The proposed thresholding function is simple and continuous. It overcomes the discontinuous shortcoming of the hard thresholding function and the disadvantage of soft thresholding function which is the invariable dispersion between the estimated wavelet coefficients and the wavelet coefficients contaminated by noise. At the same time, the clustering characteristics of wavelet coefficients are utilized effectively in new function. That is, the neighboring wavelet coefficients are incorporated into the estimation of wavelet coefficients. Simulation results show that the proposed denoising algorithm owns better visual effect and PSNR performance than many exiting thresholding methods and Matlab-wiener 2 method.
出处 《激光与红外》 CAS CSCD 北大核心 2006年第10期988-991,共4页 Laser & Infrared
基金 高等学校博士学科点专项科研基金(20050290010)
关键词 小波变换 红外图像 阈值函数 邻域小波系数 峰值信噪比 wavelet transform infrared image thresholding function neighbor wavelet coefficients PSNR
  • 相关文献

参考文献6

  • 1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
  • 2Donoho D L,Johnstone I M.Ideal Spatial Adaptation Via Wavelet Shrinkage[J].Biometika,1994,81(12):425-455.
  • 3Donoho D L.De-noising by Soft-thresholding[J].IEEE Trans.on IT,1995,41(3):613-627.
  • 4邹海林,隋亚莉,徐俊艳,宁书年.基于多小波变换的GPR图象去噪方法研究[J].系统仿真学报,2005,17(4):855-858. 被引量:16
  • 5Dongwook Cho,Tien D Bui.Multivariate statistical modeling for image denoising using wavelet transforms[J].Signal Processing:Image Communication,2005,20:77-89.
  • 6Donoho D L,Johnstone I M.Threshold selection for wavelet shrinkage of noisy data[A].In:Proc 16th Annual International Conference of IEEE Engineering in Medicine and Biology Society[C],Baltimore,Maryland.1994,1.A24-A25.

二级参考文献82

  • 1刘志刚,钱清泉.自适应阈值多小波故障暂态信号去噪方法[J].系统工程与电子技术,2004,26(7):878-880. 被引量:15
  • 2[9]You Yuli, Kaveh D. Fourth-order partial differential equations for noise removal[J]. IEEE Trans. Image Processing, 2000,9(10):1723~1730.
  • 3[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.
  • 4[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.
  • 5[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.
  • 6[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.
  • 7[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.
  • 8[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.
  • 9[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.
  • 10[17]Krishnan S, Rangayyan R M. Denoising knee joint vibration signals using adaptive time-frequency representations[A]. In:Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering 'Engineering Solutions for the Next Millennium[C]. Alberta Canada, 1999:1495~1500.

共引文献266

同被引文献254

引证文献33

二级引证文献206

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