WebJan 6, 2024 · We report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained … WebJul 30, 2024 · In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate …
Deep Learning Coherent Diffractive Imaging - Cornell University
WebDiffractive imaging of individual nanoparticles creates huge data sets which are necessary for successful structure determination. ... that deep neural networks can be used to classify large amounts of diffraction data … WebSep 11, 2024 · Deep learning enabled by neural networks is bringing a host of new opportunities for improving the reconstruction of images obtained from digital … master bakers association
(PDF) Dynamic coherent diffractive imaging with a physics …
WebJan 6, 2024 · Abstract. We report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks … WebMar 2, 2024 · We present a deep learning-based generative model for the enhancement of partially coherent diffractive images. In lensless coherent diffractive imaging, a highly coherent X-ray illumination is required to image an object at high resolution. Non-ideal experimental conditions result in a partially coherent X-ray illumination, lead to … WebMay 20, 2024 · Color transparency, the reduction of initial-state or final-state interactions in coherent nuclear processes, is a natural prediction of QCD (quantum chromodynamics) provided that small-sized or point-like configurations (PLCs) are formed in high-momentum transfer, high-energy, semi-exclusive processes. I use the Frankfurt-Miller-Strikman … hyland stable horse gear