期刊文献+

复合材料剪切散斑检测图像的小波降噪技术研究 被引量:1

Studies on Wavelet De-noising of Shearography Image for Airplane Composites
下载PDF
导出
摘要 电子剪切散斑检测技术非常适宜复合材料的无损检测。散斑图像往往含有较大的噪声,如何对散斑图像进行降噪处理是一个非常重要的问题。小波变换是变分辨率的分析方法,用小波降噪技术处理散斑图像可以有效地降低噪声,同时能较好地保存图像细节。 The shearography inspection technology is extremely fitting for the nondestructive testing of composites. The testing image of shearography includes much speckle noise, so de-noising is a very important task. The wavelet transform method is multi-resolution analysis, it can reduce noise, and at the same time, can keep the details of the image. The wavelet de-noising technique was studied. Experimental results were satisfactory.
作者 张坚 耿荣生
出处 《无损检测》 北大核心 2008年第2期94-96,103,共4页 Nondestructive Testing
基金 总装备部预研课题(41327030101)
关键词 剪切散斑 复合材料 小波变换 降噪 Shearography Composites Wavelet transform De-noising
  • 相关文献

参考文献6

  • 1[1]Parker S J,Salter P L.A novel shearography system for aerospace non-destructive testing[P].Proc Instn Mech Engrs,1999,213.
  • 2[2]Vidakovic B,Lozoya C B.On time-dependent wavelet denoising[J].IEEE Trans Signal Processing,1998,46(9):2549-2551.
  • 3[3]Daubechies I.The wavelet transform:time-frequency localization and signal analysis[J].IEEE Trans Theory,2001,26(9):961-1005.
  • 4[4]Donoho D L.Denoising by soft-thresholding[J].IEEE Trans on Information Technology,1995,41(3):613.
  • 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[6]Donoho D L,Johnstone I M.Ideal spatial adaptation via wavelet shrinkage[J].Biometrika,1994,81(3):425-455.

二级参考文献66

  • 1[9]You Yuli, Kaveh D. Fourth-order partial differential equations for noise removal[J]. IEEE Trans. Image Processing, 2000,9(10):1723~1730.
  • 2[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.
  • 3[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.
  • 4[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.
  • 5[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.
  • 6[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.
  • 7[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.
  • 8[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.
  • 9[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.
  • 10[18]Liu Bin, Wang Yuanyuan, Wang Weiqi. Spectrogram enhancement algorithm: A soft thresholding-based approach[J]. Ultrasound in Medical and Biology, 1999,25(5):839~846.

共引文献251

同被引文献19

引证文献1

二级引证文献61

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