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 superiorization schemes are evaluated. Theoretical analysis of the methods as well as computational experiments with both synthetic and real data are provided.
@article{arxiv.1608.04952,
title = {Superiorization of Incremental Optimization Algorithms for Statistical Tomographic Image Reconstruction},
author = {Elias S. Helou and Marcelo V. W. Zibetti and Eduardo X. Miqueles},
journal= {arXiv preprint arXiv:1608.04952},
year = {2019}
}