Title
Bending-Independent Imaging Through Glass-Air Disordered Fiber Based On Deep Learning
Abstract
We demonstrate a bending-independent imaging system for the first time by combining deep neural networks (DNNs) and a meter-long silica-air disordered optical fiber. High-quality artifact-free images can be reconstructed from the transported raw images.
Publication Date
1-1-2018
Publication Title
Optics InfoBase Conference Papers
Volume
Part F99-COSI 2018
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1364/COSI.2018.CW3B.6
Copyright Status
Unknown
Socpus ID
85051269020 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85051269020
STARS Citation
Zhao, Jian; Sun, Yangyang; Zhu, Zheyuan; Zheng, Donghui; and Antonio-Lopez, Jose Enrique, "Bending-Independent Imaging Through Glass-Air Disordered Fiber Based On Deep Learning" (2018). Scopus Export 2015-2019. 8018.
https://stars.library.ucf.edu/scopus2015/8018