DeepGhost: real-time computational ghost imaging via deep learning
Abstract The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for...
Enregistré dans:
Auteurs principaux: | Saad Rizvi, Jie Cao, Kaiyu Zhang, Qun Hao |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/0263495455744848bd5b08f1bf5b51bb |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Deep learning early stopping for non-degenerate ghost imaging
par: Chané Moodley, et autres
Publié: (2021) -
The ghosts in the computer: the role of agency and animacy attributions in "ghost controls".
par: Francys Subiaul, et autres
Publié: (2011) -
Counterfactual ghost imaging
par: Jonte R. Hance, et autres
Publié: (2021) -
Experimental demonstration of spectral domain computational ghost imaging
par: Piotr Ryczkowski, et autres
Publié: (2021) -
Gradient-Descent-like Ghost Imaging
par: Wen-Kai Yu, et autres
Publié: (2021)