645 citations · 992 across the 7 of their papers we have counts for
7 papers
Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
Elias Nehme, Rotem Mulayoff, Tomer Michaeli
When solving ill-posed inverse problems, one often desires to explore the space of potential solutions rather than be presented with a single plausible reconstruction. Valuable ins…
Uncertainty Visualization via Low-Dimensional Posterior Projections
Omer Yair, Elias Nehme, Tomer Michaeli
In ill-posed inverse problems, it is commonly desirable to obtain insight into the full spectrum of plausible solutions, rather than extracting only a single reconstruction. Inform…
Uncertainty Quantification via Neural Posterior Principal Components
Elias Nehme, Omer Yair, Tomer Michaeli
Uncertainty quantification is crucial for the deployment of image restoration models in safety-critical domains, like autonomous driving and biological imaging. To date, methods fo…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Learning an optimal PSF-pair for ultra-dense 3D localization microscopy
Elias Nehme, Boris Ferdman, Lucien E. Weiss +4
A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity. One established approach for snapsho…
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning
Elias Nehme, Daniel Freedman, Racheli Gordon +6
Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e.g. fluorescent molecules) are determined at high precision from the…
Deep-STORM: super-resolution single-molecule microscopy by deep learning
Elias Nehme, Lucien E. Weiss, Tomer Michaeli +1
We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent…