Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction

• Autor(a) principal: Helou, E. S. [UNESP]
• Data de Publicação: 2017
• Outros Autores: Zibetti, M. V. W., Miqueles, E. X.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1088/1361-6420/33/4/044010; http://hdl.handle.net/11449/159422
• topic: superiorization; convex optimization; tomographic image reconstruction

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.