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
The computational efficiency of numerical solution of linearalgebraic equations in finite elements can be improved in two ways.One is to decrease the fill-in numbers, which are new non-ze- ronumbers in the matrix of g...The computational efficiency of numerical solution of linearalgebraic equations in finite elements can be improved in two ways.One is to decrease the fill-in numbers, which are new non-ze- ronumbers in the matrix of global stiffness generated during theprocess of elimination. The other is to reduce the computationaloperation of multiplying a real number by zero. Based on the factthat the order of elimination can determine how many fill-in numbersshould be generated, we present a new method for optimization ofnumbering nodes. This method is quite different from bandwidthoptimiza- tion. Fill-in numbers can be decreased in a large scale bythe use of this method. The bi-factorization method is adopted toavoid multiplying real numbers by zero. For large scale finiteelement analysis, the method presented in this paper is moreefficient than the traditional LDLT method.展开更多
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.
文摘The computational efficiency of numerical solution of linearalgebraic equations in finite elements can be improved in two ways.One is to decrease the fill-in numbers, which are new non-ze- ronumbers in the matrix of global stiffness generated during theprocess of elimination. The other is to reduce the computationaloperation of multiplying a real number by zero. Based on the factthat the order of elimination can determine how many fill-in numbersshould be generated, we present a new method for optimization ofnumbering nodes. This method is quite different from bandwidthoptimiza- tion. Fill-in numbers can be decreased in a large scale bythe use of this method. The bi-factorization method is adopted toavoid multiplying real numbers by zero. For large scale finiteelement analysis, the method presented in this paper is moreefficient than the traditional LDLT method.
