Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction

Autor(es)

Helou, E. S. ; Zibetti, M. V. W. ; Miqueles, E. X.

• Tipo de documento: Artigo Científico
• Fonte: INVERSE PROBLEMS; v. 33, n. 4 APR 2017.
• Citações Web of Science: 4

Resumo

We propose the superiorization of incremental algorithms for tomographic image reconstruction. The resulting methods follow a better path in its way to finding the optimal solution for the maximum likelihood problem in the sense that they are closer to the Pareto optimal curve than the non-superiorized techniques. A new scaled gradient iteration is proposed and three super-iorization schemes are evaluated. Theoretical analysis of the methods as well as computational experiments with both synthetic and real data are provided. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: José Alberto Cuminato
• Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs