Nathaniel Mays

Nathaniel Mays, Ming Zhong

We propose and analyze an iterative refinement of a modified Lavrentiev regularization method for deconvolution of the discrete Helmholtz-type differential filter. The modification for the Lavrentiev regularization method exploits the properties of the Helmholtz filter, and we prove that the modification reduces the error bound between the original solution and the approximated solution. Furthermore, we derive an optimal stopping condition on the number of iterations necessary for the regularization. We provide numerical examples demonstrating the benefits of this iterative modified Lavrentiev regularization over a family of Tikhonov regularization methods.