107 citations · 240 across the 9 of their papers we have counts for
10 papers
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…
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning
Jeffrey M. Ede
Compressed sensing can decrease scanning transmission electron microscopy electron dose and scan time with minimal information loss. Traditionally, sparse scans used in compressed…
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…
Adaptive Learning Rate Clipping Stabilizes Learning
Jeffrey M. Ede, Richard Beanland
Artificial neural network training with stochastic gradient descent can be destabilized by "bad batches" with high losses. This is often problematic for training with small batch s…
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…
Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and Compression
Jeffrey M. Ede
We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for electron micrograph restoration and compression. These have been trained for transmission electron microsco…
Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder
Jeffrey M. Ede
We present an atrous convolutional encoder-decoder trained to denoise 512512 crops from electron micrographs. It consists of a modified Xception backbone, atrous convoltion…