小波模极大值法在地震解释图像边缘检测中的应用
|
摘要
边缘作为图像的最主要特征,是图像视觉信息获取的重要内容。小波变换具有检测信号局域突变的能力,且可以结合多尺度信息进行检测,因此成为图像边缘检测的优良工具。基于信号与噪声在不同尺度下小波系数模的变化特征,可以通过检测小波变换系数模局部极大值点来提取图像的边缘特征。实际课题中将小波模极大值法应用于三维地震资料——相干切片图像的边缘检测中,证实了该方法能较准确地提取断层边缘,为下一步对断层的提取及解释提供有力保障。
As foremost characteristic of image,edge is the important content of image vision information.Wavelet transformation can not only detect the local abrupt change of the input signal,but also detect by combining multi-scale information.Therefore,wavelet becomes the good tool for image edge detection.Due to different properties of the signal and noise in wavelet coefficient modulus under different scale of wavelet transformation,we can extract the edge characteristic through detecting the local maximum of the wavelet transformation coefficient modulus.The method has been applied in the edge detection of coherent slice image in three-dimension seismic interpretation images,and the result of the experiments has shown that the method can extract the faultage edge accurately,and provide strong ensurance for extracting and interpreting the faultage in next step.
As foremost characteristic of image,edge is the important content of image vision information.Wavelet transformation can not only detect the local abrupt change of the input signal,but also detect by combining multi-scale information.Therefore,wavelet becomes the good tool for image edge detection.Due to different properties of the signal and noise in wavelet coefficient modulus under different scale of wavelet transformation,we can extract the edge characteristic through detecting the local maximum of the wavelet transformation coefficient modulus.The method has been applied in the edge detection of coherent slice image in three-dimension seismic interpretation images,and the result of the experiments has shown that the method can extract the faultage edge accurately,and provide strong ensurance for extracting and interpreting the faultage in next step.
引文
[1]尹平,王润生.自适应多尺度边缘检测[J].软件学报,2000,11(8):990-994.
[2]MALLAT S.Zero crossing of a wavelet transform[J].IEEETransactions on Information Theory,1991,37(4):1 019-1 033.
[3]MALLAT S,HWANG W L.Singularity detection and process-ing with wavelets[J].IEEE Transactions on Information Theo-ry,1992,38(2):617-643.
[4]COHEN A.Biorthogonal wavelets and dual filter[A].Wave-lets in Image Communication[C].Valbonne:Elsevier,1994.1-26.
[5]唐世伟,林君.小波变换与中值滤波相结合图像去噪方法[J].哈尔滨工业大学学报,2008,40(8):1 334-1 336.
[6]徐继友,朱耆祥.小波变换在图象边缘提取中的应用[J].光电工程,1998,25(4):28-34.
[7]COHEN A.Biorthogonal wavelets[A].Wavelets:A Tutorial inTheory and Application[C].[S.l.]:Academic Press Ltd,1999.123-152.
[8]任伟建,康朝海,于镝,等.小波变换在信号奇异性检测中的应用[J].自动化技术与应用,2005,24(1):68-69.
[2]MALLAT S.Zero crossing of a wavelet transform[J].IEEETransactions on Information Theory,1991,37(4):1 019-1 033.
[3]MALLAT S,HWANG W L.Singularity detection and process-ing with wavelets[J].IEEE Transactions on Information Theo-ry,1992,38(2):617-643.
[4]COHEN A.Biorthogonal wavelets and dual filter[A].Wave-lets in Image Communication[C].Valbonne:Elsevier,1994.1-26.
[5]唐世伟,林君.小波变换与中值滤波相结合图像去噪方法[J].哈尔滨工业大学学报,2008,40(8):1 334-1 336.
[6]徐继友,朱耆祥.小波变换在图象边缘提取中的应用[J].光电工程,1998,25(4):28-34.
[7]COHEN A.Biorthogonal wavelets[A].Wavelets:A Tutorial inTheory and Application[C].[S.l.]:Academic Press Ltd,1999.123-152.
[8]任伟建,康朝海,于镝,等.小波变换在信号奇异性检测中的应用[J].自动化技术与应用,2005,24(1):68-69.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心