Image reconstruction and restoration using the simplified topological ε-algorithm

Silvia Gazzola, Anna Karapiperi

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)


In order to compute meaningful approximations of the solutions of large-scale linear inverse ill-posed problems, some form of regularization should be employed. Cimmino and Landweber methods are well-known iterative regularization methods that can be quite successfully applied for tomographic reconstruction and image restoration problems, despite their usually slow convergence. The goal of this paper is to explore the performance of a recent extrapolation algorithm when applied to accelerate the convergence of these iterative regularization methods. In particular, we provide insight and algorithmic details about the simplified topological ε-algorithm applied to slow-converging iterative regularization methods. The results of many numerical experiments and comparisons with other methods are also displayed.

Original languageEnglish
Pages (from-to)539-555
Number of pages17
JournalApplied Mathematics and Computation
Publication statusPublished - 1 Feb 2016


  • Extrapolation methods
  • Image reconstruction
  • Image restoration
  • Projected SIRT methods
  • SIRT methods
  • ε-algorithms

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics


Dive into the research topics of 'Image reconstruction and restoration using the simplified topological ε-algorithm'. Together they form a unique fingerprint.

Cite this