Abstract
An iterative method for a class of image reconstruction problems which lead to large scale optimization problems is presented. The method uses a regularization of the objective functional and is based on its dual formulation which is a semi-separable convex minimization problem with linear constraints, where the function to be minimized is the sum of a Burg's entropy and a quadratic function. From the special structure of this new formulation in combination with a Bregman type method, a computationally attractive algorithm emerges and its convergence properties are proved.
| Original language | English |
|---|---|
| Article number | 006 |
| Pages (from-to) | 679-696 |
| Number of pages | 18 |
| Journal | Inverse Problems |
| Volume | 9 |
| Issue number | 6 |
| DOIs | |
| State | Published - 1993 |