Abstract
The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning (DL) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets. Using 3D X-ray Bragg coherent diffraction imaging (BCDI) as a representative technique, we demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase problem without labeled data. By incorporating the imaging physics into the DL model during training, AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images. Once trained, AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100× faster than iterative phase retrieval methods while providing comparable image quality.
| Original language | English |
|---|---|
| Article number | 124 |
| Journal | npj Computational Materials |
| Volume | 8 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
| Externally published | Yes |
Funding
This work was performed, in part, at the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided on Swing, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program under Award Number 34532. M.J.C. acknowledges partial support from Argonne LDRD 2021-0090—AutoPtycho: Autonomous, Sparse-sampled Ptychographic Imaging. Y.Y. acknowledges partial support from Argonne LDRD 2021-0315—Scalable DL-based 3D X-ray nanoscale imaging enabled by AI accelerators. This work was performed, in part, at the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided on Swing, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program under Award Number 34532. M.J.C. acknowledges partial support from Argonne LDRD 2021-0090—AutoPtycho: Autonomous, Sparse-sampled Ptychographic Imaging. Y.Y. acknowledges partial support from Argonne LDRD 2021-0315—Scalable DL-based 3D X-ray nanoscale imaging enabled by AI accelerators.