A new approach to improve optimizer performance through algorithms diversification for image reconstruction in diffuse optical tomography
Abstract
In this work, we explore a different way to construct optimizer algorithms for solving the inverse problem of Diffuse Optical Tomography by using diversification of two stochastic gradient-based algorithms, namely NADAM and AMSGrad. We will study the speed of convergence of the proposed new breed of algorithms, also we will discuss the quality of reconstructed images in both cases of free of noise and noisy measurement data. For analysis and exploration of the potential of the proposed algorithm, we use statistical simulations and analysis approach.
- Published
- 24 November 2021
- Volume
- 2021 (2021)
- License
- CC BY 3.0
Copyright © 2021 Nada Chakhim, Mohamed Louzar, Mohammed Alaoui. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
N. Chakhim, M. Louzar, M. Alaoui, A new approach to improve optimizer performance through algorithms diversification for image reconstruction in diffuse optical tomography, Commun. Math. Biol. Neurosci. 2021 (2021), 89. https://doi.org/10.28919/cmbn/6835
BibTeX
@article{Chakhim2021,
author = {Chakhim, Nada and Louzar, Mohamed and Alaoui, Mohammed},
title = {A new approach to improve optimizer performance through algorithms diversification for image reconstruction in diffuse optical tomography},
journal = {Commun. Math. Biol. Neurosci.},
volume = {2021},
year = {2021},
pages = {89},
doi = {10.28919/cmbn/6835}
}