1.1k citations · 2.2k across the 4 of their papers we have counts for
4 papers
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Yichen Wu, Yair Rivenson, Yibo Zhang +4
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s),…
Deep learning enhanced mobile-phone microscopy
Yair Rivenson, Hatice Ceylan Koydemir, Hongda Wang +8
Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the o…
Deep Learning Microscopy
Yair Rivenson, Zoltan Gorocs, Harun Gunaydin +3
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its tr…
Phase recovery and holographic image reconstruction using deep learning in neural networks
Yair Rivenson, Yibo Zhang, Harun Gunaydin +2
Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. Here we demonstrate that a neural network can learn to perform phase…