微小运动放大及其在超声波传播中的应用
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摘要
针对在具有复杂残余应力的材料中超声波传播视频信号极其微弱、难以看清其传播过程的问题,采用微小运动放大方法揭示超声波传播视频中人眼难以看见的微小运动,研究了线性欧拉视频放大方法(EVM)和基于相位的视频运动放大方法(PBVM)在超声波传播中的应用.提出了基于可操纵金字塔的微小运动放大方法,通过对一段标准视频序列进行可操纵金字塔空间分解、时域滤波,放大得到的信号以揭示微小的运动.实验结果表明:该方法在图像噪声方面比EVM更不敏感,在计算时间方面该方法比PBVM用时更短.对EVM,PBVM和本方法进行了对比验证,并在超声波传播视频中进行了应用,效果良好.提出的方法也可应用于机械和结构的状态检测中.
Ultrasonic propagation of video signals in complex materials with complex residual stresses is extremely weak and it is difficult to visualise the propagation process,to deal with this problem,the small motion magnification method was used to reveal the small movement difficult or impossible for human eye to see in the ultrasonic propagation video, and the application of linear Eulerian video magnification(EVM) and phase-based video motion magnification(PBVM) in ultrasonic propagation was studied.A technique named small motion magnification based on the steerable pyramid was introduced, the method took a standard video sequence signal as input,and applied spatial decomposition through the steerable pyramid,followed by temporal bandpass filtering,and the obtained signal was amplified to reveal subtle movements. Experiments show that the algorithm is less sensitive to image noise in comparison to the Eulerian video magnification(EVM) method, and that it is more efficient than the phase-based video motion(PBVM) method in computing time, EVM, PBVM, and our method in the propagation process of ultrasonic wave were compared, and the effect is good. The method presented in this paper can also be applied to the mechanical and structural state detection.
Ultrasonic propagation of video signals in complex materials with complex residual stresses is extremely weak and it is difficult to visualise the propagation process,to deal with this problem,the small motion magnification method was used to reveal the small movement difficult or impossible for human eye to see in the ultrasonic propagation video, and the application of linear Eulerian video magnification(EVM) and phase-based video motion magnification(PBVM) in ultrasonic propagation was studied.A technique named small motion magnification based on the steerable pyramid was introduced, the method took a standard video sequence signal as input,and applied spatial decomposition through the steerable pyramid,followed by temporal bandpass filtering,and the obtained signal was amplified to reveal subtle movements. Experiments show that the algorithm is less sensitive to image noise in comparison to the Eulerian video magnification(EVM) method, and that it is more efficient than the phase-based video motion(PBVM) method in computing time, EVM, PBVM, and our method in the propagation process of ultrasonic wave were compared, and the effect is good. The method presented in this paper can also be applied to the mechanical and structural state detection.
引文
[1]刘元锋.固体中超声散射的数值模拟和动态光弹技术研究[D].华中科技大学机械科学与工程学院,2013.
[2]王飞橙.固体中缺陷的超声散射计算与测量技术研究[D].华中科技大学机械科学与工程学院,2014.
[3]Liu C,Torralba A,Freeman W T,et al.Motion magnification[J].Acm Transactions on Graphics,2005,24(3):519-526.
[4]Wu H Y,Rubinstein M,Shih E,et al.Eulerian video magnification for revealing subtle changes in the world[J].Acm Transactions on Graphics,2012,31(4):1-8.
[5]Wadhwa N,Rubinstein M,Freeman W T.Phase-based video motion processing[J].Acm Transactions on Graphics,2013,32(4):1-10.
[6]Wadhwa N,Rubinstein M,Durand F,et al.Riesz pyramids for fast phase-based video magnification[C]//IEEE Inter-national Conference on Computational Photography.Santa Clara:IEEE,2014:1-10.
[1]Simoncelli E P,Freeman W T.The steerable pyramid:a flexible architecture for multi-scale derivative computation[C]//Proc of International Conference on Image Processing.Washington DC:IEEE Computer Society,1995:444-447.
[1]Karasaridis A,Simoncelli E.A filter design technique for steerable pyramid image transforms[C]//Proc of IEEE International Conference on Acoustics,Speech,and Signal Processing.Atlanta:IEEE,1996:2387-2390.
[1]Burt P J,Adelson E H.The Laplacian pyramid as a compact image code[J].Communications IEEE Transactions on,1983,31(4):532-540.
[1]Horn B K P,Schunck B G.Determining optical flow[J].Artificial Intelligence,1981,17(1-3):185-203.
[2]Rubinstein M.Analysis and visualization of temporal variations in video[D].Boston:Department of Electrical Engineering and Computer Science,Massachusetts Institute of Technology,2014.
[3]Portilla J,Simoncelli E P.A parametric texture model based on joint statistics of complex wavelet coefficients[J].International Journal of Computer Vision,2000,40(1):49-70.
[2]王飞橙.固体中缺陷的超声散射计算与测量技术研究[D].华中科技大学机械科学与工程学院,2014.
[3]Liu C,Torralba A,Freeman W T,et al.Motion magnification[J].Acm Transactions on Graphics,2005,24(3):519-526.
[4]Wu H Y,Rubinstein M,Shih E,et al.Eulerian video magnification for revealing subtle changes in the world[J].Acm Transactions on Graphics,2012,31(4):1-8.
[5]Wadhwa N,Rubinstein M,Freeman W T.Phase-based video motion processing[J].Acm Transactions on Graphics,2013,32(4):1-10.
[6]Wadhwa N,Rubinstein M,Durand F,et al.Riesz pyramids for fast phase-based video magnification[C]//IEEE Inter-national Conference on Computational Photography.Santa Clara:IEEE,2014:1-10.
[1]Simoncelli E P,Freeman W T.The steerable pyramid:a flexible architecture for multi-scale derivative computation[C]//Proc of International Conference on Image Processing.Washington DC:IEEE Computer Society,1995:444-447.
[1]Karasaridis A,Simoncelli E.A filter design technique for steerable pyramid image transforms[C]//Proc of IEEE International Conference on Acoustics,Speech,and Signal Processing.Atlanta:IEEE,1996:2387-2390.
[1]Burt P J,Adelson E H.The Laplacian pyramid as a compact image code[J].Communications IEEE Transactions on,1983,31(4):532-540.
[1]Horn B K P,Schunck B G.Determining optical flow[J].Artificial Intelligence,1981,17(1-3):185-203.
[2]Rubinstein M.Analysis and visualization of temporal variations in video[D].Boston:Department of Electrical Engineering and Computer Science,Massachusetts Institute of Technology,2014.
[3]Portilla J,Simoncelli E P.A parametric texture model based on joint statistics of complex wavelet coefficients[J].International Journal of Computer Vision,2000,40(1):49-70.