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

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.