Deep-Learning-Based Imaging Through Glass-Air Disordered Fiber With Transverse Anderson Localization
Abstract
We demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images transported through a 90 cm-long silica-air disordered optical fiber at variable working distances without any distal optics.
Publication Date
8-6-2018
Publication Title
2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
85052553881 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85052553881
STARS Citation
Zhao, Jian; Sun, Yangyang; Zhu, Zheyuan; Zheng, Donghui; and Antonio-Lopez, Jose Enrique, "Deep-Learning-Based Imaging Through Glass-Air Disordered Fiber With Transverse Anderson Localization" (2018). Scopus Export 2015-2019. 7835.
https://stars.library.ucf.edu/scopus2015/7835
COinS