1D inverse problem in diffusion based optical tomography using iteratively regularized Gauss–Newton algorithm

摘要

In this paper, we investigate an one-dimensional inverse problem in diffusion based optical tomography using iteratively regularized Gauss–Newton (IRGN) algorithm for ill-posed nonlinear problems. We devise a stable reconstruction algorithm for the inverse problem using iterative regularization with Armijo–Goldstein–Wolf (AGW) type line search strategy. We demonstrate the efficacy of the IRGN combined with AGW by reconstructing the scattering parameter relevant to the inverse problem in optical tomography.