5 citations · 7 across the 4 of their papers we have counts for
3 papers · 1 filter
How to do Physics-based Learning
Michael Kellman, Michael Lustig, Laura Waller
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system. We provide a basic overvie…
Memory-efficient Learning for Large-scale Computational Imaging
Michael Kellman, Kevin Zhang, Jon Tamir +3
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classic…
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network
Emrah Bostan, Reinhard Heckel, Michael Chen +2
Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity,…