Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction

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

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction

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Author(s):	Helou, E. S. ; Zibetti, M. V. W. ; Miqueles, E. X. Total Authors: 3
Document type:	Journal article
Source:	INVERSE PROBLEMS; v. 33, n. 4 APR 2017.
Web of Science Citations:	4
Abstract
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)

FAPESP's process:	13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:	Francisco Louzada Neto
Support Opportunities:	Research Grants - Research, Innovation and Dissemination Centers - RIDC

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