Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some are...Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise (AWGN),and some kinds of geometric attacks such as scaling,rotation,etc.展开更多
Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal samplin...Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal sampling rate and the quantity of sampling data.However,the pulse number of the signal must be known beforehand for the signal reconstruction procedure.The accuracy of this prior information directly affects the accuracy of the estimated parameters of the signal and influences the assessment of flaws,leading to a lower defect detection ratio.Although the pulse number can be pre-given by theoretical analysis,the process is still unable to assess actual complex random orientation defects.Therefore,this paper proposes a new method that uses singular value decomposition(SVD) for estimating the pulse number from sparse sampling data and avoids the shortcoming of providing the pulse number in advance for signal reconstruction.When the sparse sampling data have been acquired from the ultrasonic signal,these data are transformed to discrete Fourier coefficients.A Hankel matrix is then constructed from these coefficients,and SVD is performed on the matrix.The decomposition coefficients reserve the information of the pulse number.When the decomposition coefficients generated by noise according to noise level are removed,the number of the remaining decomposition coefficients is the signal pulse number.The feasibility of the proposed method was verified through simulation experiments.The applicability was tested in ultrasonic experiments by using sample flawed pipelines.Results from simulations and real experiments demonstrated the efficiency of this method.展开更多
基金supported by a grant from the National High Technology Research and Development Program of China (863 Program) (No.2008AA04A107)supported by a grant from the Major Programs of Guangdong-Hongkong in the Key Domain (No.2009498B21)
文摘Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise (AWGN),and some kinds of geometric attacks such as scaling,rotation,etc.
基金Supported by the National Natural Science Foundation of China(Grant No.51375217)
文摘Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal sampling rate and the quantity of sampling data.However,the pulse number of the signal must be known beforehand for the signal reconstruction procedure.The accuracy of this prior information directly affects the accuracy of the estimated parameters of the signal and influences the assessment of flaws,leading to a lower defect detection ratio.Although the pulse number can be pre-given by theoretical analysis,the process is still unable to assess actual complex random orientation defects.Therefore,this paper proposes a new method that uses singular value decomposition(SVD) for estimating the pulse number from sparse sampling data and avoids the shortcoming of providing the pulse number in advance for signal reconstruction.When the sparse sampling data have been acquired from the ultrasonic signal,these data are transformed to discrete Fourier coefficients.A Hankel matrix is then constructed from these coefficients,and SVD is performed on the matrix.The decomposition coefficients reserve the information of the pulse number.When the decomposition coefficients generated by noise according to noise level are removed,the number of the remaining decomposition coefficients is the signal pulse number.The feasibility of the proposed method was verified through simulation experiments.The applicability was tested in ultrasonic experiments by using sample flawed pipelines.Results from simulations and real experiments demonstrated the efficiency of this method.
