107 citations · 240 across the 9 of their papers we have counts for
6 papers · 1 filter
Advances in Electron Microscopy with Deep Learning
Jeffrey M. Ede
This doctoral thesis covers some of my advances in electron microscopy with deep learning. Highlights include a comprehensive review of deep learning in electron microscopy; large…
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited…
Warwick Electron Microscopy Datasets
Jeffrey M. Ede
Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electr…
Exit Wavefunction Reconstruction from Single Transmission Electron Micrographs with Deep Learning
Jeffrey M. Ede, Jonathan J. P. Peters, Jeremy Sloan +1
Half of wavefunction information is undetected by conventional transmission electron microscopy (CTEM) as only the intensity, and not the phase, of an image is recorded. Following…
Deep Learning Supersampled Scanning Transmission Electron Microscopy
Jeffrey M. Ede
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron microscope point-scan systems with minimal information loss. Building on a history…
Partial Scanning Transmission Electron Microscopy with Deep Learning
Jeffrey M. Ede, Richard Beanland
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep…