1.1k citations · 3.8k across the 14 of their papers we have counts for
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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…
Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront
Miu Tamamitsu, Yibo Zhang, Hongda Wang +2
The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsit…
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…