Yair Rivenson
Cited by
Cited by
All-optical machine learning using diffractive deep neural networks
X Lin, Y Rivenson, NT Yardimci, M Veli, Y Luo, M Jarrahi, A Ozcan
Science 361 (6406), 1004-1008, 2018
Phase recovery and holographic image reconstruction using deep learning in neural networks
Y Rivenson, Y Zhang, H Günaydın, D Teng, A Ozcan
Light: Science & Applications 7 (2), 17141-17141, 2018
Deep learning enables cross-modality super-resolution in fluorescence microscopy
H Wang, Y Rivenson, Y Jin, Z Wei, R Gao, H Günaydın, LA Bentolila, ...
Nature methods 16 (1), 103-110, 2019
Deep learning microscopy
Y Rivenson, Z Göröcs, H Günaydin, Y Zhang, H Wang, A Ozcan
Optica 4 (11), 1437-1443, 2017
Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
Y Rivenson, H Wang, Z Wei, K de Haan, Y Zhang, Y Wu, H Günaydın, ...
Nature biomedical engineering 3 (6), 466-477, 2019
Roadmap on optical security
B Javidi, A Carnicer, M Yamaguchi, T Nomura, E Pérez-Cabré, MS Millán, ...
Journal of Optics 18 (8), 083001, 2016
PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
Y Rivenson, T Liu, Z Wei, Y Zhang, K de Haan, A Ozcan
Light: Science & Applications 8 (1), 23, 2019
Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery
Y Wu, Y Rivenson, Y Zhang, Z Wei, H Günaydin, X Lin, A Ozcan
Optica 5 (6), 704-710, 2018
Deep learning in holography and coherent imaging
Y Rivenson, Y Wu, A Ozcan
Light: Science & Applications 8 (1), 85, 2019
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Y Wu, Y Rivenson, H Wang, Y Luo, E Ben-David, LA Bentolila, C Pritz, ...
Nature methods 16 (12), 1323-1331, 2019
Compressive fresnel holography
Y Rivenson, A Stern, B Javidi
Journal of Display Technology 6 (10), 506-509, 2010
Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains
Y August, C Vachman, Y Rivenson, A Stern
Applied optics 52 (10), D46-D54, 2013
Compressed imaging with a separable sensing operator
Y Rivenson, A Stern
IEEE Signal Processing Letters 16 (6), 449-452, 2009
Design of task-specific optical systems using broadband diffractive neural networks
Y Luo, D Mengu, NT Yardimci, Y Rivenson, M Veli, M Jarrahi, A Ozcan
Light: Science & Applications 8 (1), 112, 2019
Deep learning enhanced mobile-phone microscopy
Y Rivenson, H Ceylan Koydemir, W Hongda, Z Wei, Z Ren, H Gunaydin, ...
arXiv preprint arXiv:1712.04139, 2017
Analysis of diffractive optical neural networks and their integration with electronic neural networks
D Mengu, Y Luo, Y Rivenson, A Ozcan
IEEE Journal of Selected Topics in Quantum Electronics 26 (1), 1-14, 2019
A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples
Z Gӧrӧcs, M Tamamitsu, V Bianco, P Wolf, S Roy, K Shindo, K Yanny, ...
Light: Science & Applications 7 (1), 66, 2018
Overview of compressive sensing techniques applied in holography
Y Rivenson, A Stern, B Javidi
Applied optics 52 (1), A423-A432, 2013
Speckle denoising in digital holography by nonlocal means filtering
A Uzan, Y Rivenson, A Stern
Applied optics 52 (1), A195-A200, 2013
Deep learning-based transformation of H&E stained tissues into special stains
K de Haan, Y Zhang, JE Zuckerman, T Liu, AE Sisk, MFP Diaz, KY Jen, ...
Nature communications 12 (1), 4884, 2021
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