Abstract
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 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.
Original language | English (US) |
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Pages (from-to) | 1657-1661 |
Number of pages | 5 |
Journal | IEEE Transactions on Image Processing |
Volume | 8 |
Issue number | 11 |
DOIs | |
State | Published - 1999 |
Keywords
- Image reconstruction
- Image recovery
- Image restoration
- Inverse problems
- Optical tomography
- Regularization
- Tomographic imaging
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design