Journal
SCIENTIFIC REPORTS
Volume 8, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-018-34525-1
Keywords
-
Categories
Funding
- Argonne LDRD [2018-019-N0]
- U.S. Department of Energy, Office of Science [DE-AC02-06CH11357]
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-06CH11357]
Ask authors/readers for more resources
Phase retrieval, or the process of recovering phase information in reciprocal space to reconstruct images from measured intensity alone, is the underlying basis to a variety of imaging applications including coherent diffraction imaging (CDI). Typical phase retrieval algorithms are iterative in nature, and hence, are time-consuming and computationally expensive, making real-time imaging a challenge. Furthermore, iterative phase retrieval algorithms struggle to converge to the correct solution especially in the presence of strong phase structures. In this work, we demonstrate the training and testing of CDI NN, a pair of deep deconvolutiona I networks trained to predict structure and phase in real space of a 2D object from its corresponding far-field diffraction intensities alone. Once trained, CDI NN can invert a diffraction pattern to an image within a few milliseconds of compute time on a standard desktop machine, opening the door to real-time imaging.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available