| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
Deep Neural Network Facilitated Complex Imaging of Phase Domains |
| Author(s) |
Longlong Wu, Pavol Juhas, Shinjae Yoo, Ian Robinsion |
| On-Site Speaker (Planned) |
Longlong Wu |
| Abstract Scope |
Single-particle imaging by using inversion of coherent x-ray diffraction was put forward more than decades ago. Phase retrieval methods for the reconstruction of a single particle image from the modulus of its Fourier transform have been extensively applied in X-ray Structural Science. Here, we will present a deep neural work model, which gives a rapid and accurate estimate of the complex single-particle image. We demonstrate a way to combine the model with conventional iterative methods to refine the accuracy of the reconstructed results starting from the proposed deep neural work model. This developed deep neural network model opens up opportunities for fundamental research on using Machine Learning to do phase retrieval at high speed and accuracy. This is important for real-time inversion of coherent x-ray diffraction patterns for ultrafast time-resolved studies at XFELs as well as strong-phase objects where the phase domains found inside crystals by Bragg Coherent Diffraction Imaging. |
| Proceedings Inclusion? |
Planned: |