Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Coherent Diffractive Imaging   We report the development of deep learning coherent electron diffractive
imaging at sub-angstrom resolution using convolutional neural networks (CNNs)
trained with only simulated data. We experimentally demonstrate this method by
applying the trained CNNs to directly recover the phase images from electron
diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and
a Au nanoparticle with comparable quality to those reconstructed by a
conventional ptychographic method. Fourier ring correlation between the CNN and
ptychographic images indicates the achievement of a spatial resolution in the
range of 0.70 and 0.55 angstrom (depending on different resolution criteria).
The ability to replace iterative algorithms with CNNs and perform real-time
imaging from coherent diffraction patterns is expected to find broad
applications in the physical and biological sciences.

25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs