Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like...Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.展开更多
This paper developed a fast and adaptive method for SAR complex image denoising based on lk norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ...This paper developed a fast and adaptive method for SAR complex image denoising based on lk norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ill-posed inverse problem via convex half-quadratic regularization, and compare the difference between the estimator variance obtained from the iterative formula and biased CramerRao bound, which proves the theoretic flaw of the existent methods of parameter selection. Then, the analytic expression of the model solution as the function with respect to the regularization parameter is obtained. On this basis, we study the method for selecting the regularization parameter through minimizing mean-square error of estimators and obtain the final analytic expression, which resulted in the direct calculation, high processing speed, and adaptability. Finally, the effect of regularization parameter selection on the resolution of point targets is analyzed. The experiment results of simulation and real complex-valued SAR images illustrate the validity of the proposed method.展开更多
基金Supported by Project of National Natural Science Foundation of China(No.41004041)
文摘Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.
基金Supported by the National Natural Science Foundation of China (Grant No. 60572136)the Fundamental Research Fund of NUDT (Grant No.JC0702005)
文摘This paper developed a fast and adaptive method for SAR complex image denoising based on lk norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ill-posed inverse problem via convex half-quadratic regularization, and compare the difference between the estimator variance obtained from the iterative formula and biased CramerRao bound, which proves the theoretic flaw of the existent methods of parameter selection. Then, the analytic expression of the model solution as the function with respect to the regularization parameter is obtained. On this basis, we study the method for selecting the regularization parameter through minimizing mean-square error of estimators and obtain the final analytic expression, which resulted in the direct calculation, high processing speed, and adaptability. Finally, the effect of regularization parameter selection on the resolution of point targets is analyzed. The experiment results of simulation and real complex-valued SAR images illustrate the validity of the proposed method.
