叠前非局部平均滤波压制随机噪音
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摘要
非局部平均滤波方法的去噪性能优异,但其在地震资料处理中的应用刚刚起步。该方法利用数据具有的结构冗余,以包含局部结构的小窗口或邻域为单元,利用局部结构相似性进行加权运算,增强有效信号,压制随机噪音。针对叠前地震资料数据量大、噪音背景强、局部结构简单;原始非局部平均算法对每一点滤波,需要对数据体内所有点计算权系数后进行加权计算,计算量大,对强噪音背景适用性差等不足,对原始非局部平均算法进行了改进,主要包括:基于速度谱的搜索窗口分割;基于梯度域奇异值分解的局部结构相似集选择方法;基于相似集大小的自适应滤波参数选择方法。试验结果表明,该方法改进后对于叠前地震数据的随机噪声具有较好的压制作用。
The nonlocal means method has good denoising performance, but its application is newly developing in seismic data processing. The method, using the structural redundancy of data, taking the small window with local structure and neighborhood as unit, conducts weighted arithmetic by using local structural similarity to enhance effective signals and to depress random noises. Aiming at huge amount of pre-stack seismic data, strong background noise and simple local structure, the original nonlocal means method filters each point, conducts weighted calculation after calculating the weight coefficient of all points within data. Because of short points such as huge computation volume and poor adaptability to strong noise background, the original nonlocal means method has been improved. Three modifications have been proposed for the nonlocal means algorithm. Firstly, the scan windows are divided with velocity spectrum; then, pre-selection of similar set is based on singular value decomposition in gradient domain; lastly, selection of self-adaptive filtering parameter is based on the scale of similar set. De-noising results for the test data demonstrate that the method can effectively depress the random noise of seismic data.
The nonlocal means method has good denoising performance, but its application is newly developing in seismic data processing. The method, using the structural redundancy of data, taking the small window with local structure and neighborhood as unit, conducts weighted arithmetic by using local structural similarity to enhance effective signals and to depress random noises. Aiming at huge amount of pre-stack seismic data, strong background noise and simple local structure, the original nonlocal means method filters each point, conducts weighted calculation after calculating the weight coefficient of all points within data. Because of short points such as huge computation volume and poor adaptability to strong noise background, the original nonlocal means method has been improved. Three modifications have been proposed for the nonlocal means algorithm. Firstly, the scan windows are divided with velocity spectrum; then, pre-selection of similar set is based on singular value decomposition in gradient domain; lastly, selection of self-adaptive filtering parameter is based on the scale of similar set. De-noising results for the test data demonstrate that the method can effectively depress the random noise of seismic data.
引文
[1]崔树果,朱凌燕,王建花.f-x域Cadzow技术分块压制随机噪声及其应用[J].石油物探,2012,51(1):44–49.
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[9]许录平.数字图像处理[M].北京:科学出版社,2007.
[10]刘涛.小波域中的非局部平均去噪算法研究[D].西安:西安电子科技大学,2010.
[2]MAHMOUDI M,SAPIRO G.Fast image and video denoising via nonlocal means of similar neighborhoods[J].IEEE Signal Processing Letters,2005,12(12):839–842.
[3]COUPE P,YGER P,PRIMA S,et al.An optimized blockwise nonlocal means denoising filter for 3-D magnetic images[J].IEEE Transactions on Medical Imaging,2008,27(4):425–441.
[4]VIGNESH R,OH B T,KUO C C J.Fest non-local means(NLM)computation with probabilistic early termination[J].IEEE Signal Processing Letters,2010,17(3):277–280.
[5]TASDIZEN T.Principal neighborhood dictionaries for nonlocal means image denoising[J].IEEE Transactions on Image Processing,2009,18(12):2649–2660.
[6]BROX T,KLEINSCHMIDT O,CREMERS D.Efficient nonlocal means for denoising of textural patterns[J].IEEE Transactions on Image Processing,2008,17(7):1083–1092.
[7]许光宇,谭结庆,钟金琴.有效保持细节特征的快速非局部滤波方法[J].计算机工程与应用,2012,48(23):196–202.
[8]余松煜.数字图像处理[M].北京:电子工业出版社,1989:18–57.
[9]许录平.数字图像处理[M].北京:科学出版社,2007.
[10]刘涛.小波域中的非局部平均去噪算法研究[D].西安:西安电子科技大学,2010.