Cargando…
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging. However, the generalization of their image reconstruction performance to new types of samples never seen by the network remains a challenge. Here we introduce a deep learning...
Autores principales: | Chen, Hanlong, Huang, Luzhe, Liu, Tairan, Ozcan, Aydogan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9378708/ https://www.ncbi.nlm.nih.gov/pubmed/35970839 http://dx.doi.org/10.1038/s41377-022-00949-8 |
Ejemplares similares
-
Recurrent neural network-based volumetric fluorescence microscopy
por: Huang, Luzhe, et al.
Publicado: (2021) -
Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack
por: Kang, Ji-Won, et al.
Publicado: (2021) -
Phase recovery and holographic image reconstruction using deep learning in neural networks
por: Rivenson, Yair, et al.
Publicado: (2018) -
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
por: Zhang, Yijie, et al.
Publicado: (2021) -
Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks
por: Karantzas, Nikos, et al.
Publicado: (2022)