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- W4306180868 abstract "The weighted least squares algorithm is effective in image reconstruction, in which the weight is determined by a covariance matrix of model error. To speed up the iterative convergence speed of the WLS method, we express the WLS iterative formula in a matrix form with an iterative relaxation coefficient and propose relaxation strategies based on minimizing the spectral radius of the iterative matrix. Numerical simulations demonstrate that the relaxation strategies proposed in this paper have better reconstruction performance. • In order to speed up the iterative convergence speed of WLS method, The gradient descent method is used to solve the nonlinear objective function. • We express the WLS iterative formula in a matrix form with an iterative relaxation coefficient, and propose a relaxation strategy based on minimizing the spectral radius of the iterative matrix. • Through numerical simulation and error analysis, it can be concluded that the new method proposed in this paper has a faster iterative convergence rate than the previous iterative algorithm." @default.
- W4306180868 created "2022-10-14" @default.
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- W4306180868 date "2023-02-01" @default.
- W4306180868 modified "2023-09-29" @default.
- W4306180868 title "Accelerated convergence strategy of weighted least squares method for image reconstruction" @default.
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- W4306180868 doi "https://doi.org/10.1016/j.cnsns.2022.106938" @default.
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