## Abstract

The sparse vector solutions for an underdetermined system of linear equations Ax = b have many applications in signal recovery and image reconstruction in tomography. Under certain conditions, the sparsest solution can be found by solving a constrained l₁ minimization problem: min ||x||₁ subject to Ax = b. Recently, the reweighted l₁ minimization and l₁ greedy algorithm have been introduced to improve the convergence of the l₁ minimization problem. As an extension, a generalized l₁ greedy algorithm for computerized tomography (CT) is proposed in this paper. It is implemented as a generalized total variation minimization for images with sparse gradients in CT. A numerical experiment is also given to illustrate the advantage of the new algorithm.

Original language | American English |
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State | Published - Jan 9 2013 |

Event | Joint Mathematics Meetings (JMM) - Duration: Jan 6 2017 → … |

### Conference

Conference | Joint Mathematics Meetings (JMM) |
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Period | 01/6/17 → … |

## Keywords

- Computed tomography
- Greedy algorithm

## DC Disciplines

- Mathematics

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