Regularized total least squares approach for nonconvolutional linear inverse problems

Wenwu Zhu, Yao Wang, N.P. Galatsanos, Jun Zhang
1999 IEEE Transactions on Image Processing  
In this correspondence, a solution is developed for the regularized total least squares (RTLS) estimate in linear inverse problems where the linear operator is nonconvolutional. Our approach is based on a Rayleigh quotient (RQ) formulation of the TLS problem, and we accomplish regularization by modifying the RQ function to enforce a smooth solution. A conjugate gradient algorithm is used to minimize the modified RQ function. As an example, the proposed approach has been applied to the
more » ... ed to the perturbation equation encountered in optical tomography. Simulation results show that this method provides more stable and accurate solutions than the regularized least squares and a previously reported total least squares approach, also based on the RQ formulation.
doi:10.1109/83.799895 pmid:18267442 fatcat:zsgrmfio4fh45hgv5jwwsjzjgu