Iterative Refinement of A Modified Lavrentiev Regularization Method for De-convolution of the Discrete Helmholtz Type Differential Filter
For researchers in inverse problems and image processing, this provides an improved regularization technique for Helmholtz filter deconvolution, though it is an incremental improvement over existing methods.
The paper proposes an iterative refinement of a modified Lavrentiev regularization method for deconvolution of the discrete Helmholtz-type differential filter, proving reduced error bounds and optimal stopping criteria, with numerical examples showing benefits over Tikhonov methods.
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.