Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1140894 | Mathematics and Computers in Simulation | 2010 | 14 Pages |
Abstract
The present paper is a theoretical contribution to the field of iterative methods for solving inconsistent linear least squares problems arising in image reconstruction from projections in computerized tomography. It consists on a hybrid algorithm which includes in each iteration a CG-like step for modifying the right-hand side and a Kaczmarz-like step for producing the approximate solution. We prove convergence of the hybrid algorithm for general inconsistent and rank-deficient least-squares problems. Although the new algorithm has potential for more applied experiments and comparisons, we restrict them in this paper to a regularized image reconstruction problem involving a 2D medical data set.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Constantin Popa,