1. Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks
- Author
-
Jiecheng Diao, Shinjae Yoo, Longlong Wu, Wonsuk Cha, Ian K. Robinson, Ross Harder, Tadesse Assefa, and Ana F. Suzana
- Subjects
Diffraction ,Computer science ,FOS: Physical sciences ,Applied Physics (physics.app-ph) ,02 engineering and technology ,01 natural sciences ,Convolutional neural network ,QA76.75-76.765 ,0103 physical sciences ,FOS: Electrical engineering, electronic engineering, information engineering ,Oversampling ,General Materials Science ,Computer software ,010306 general physics ,Materials of engineering and construction. Mechanics of materials ,Condensed Matter - Materials Science ,Image and Video Processing (eess.IV) ,Supervised learning ,Materials Science (cond-mat.mtrl-sci) ,Disordered Systems and Neural Networks (cond-mat.dis-nn) ,Physics - Applied Physics ,Electrical Engineering and Systems Science - Image and Video Processing ,Condensed Matter - Disordered Systems and Neural Networks ,021001 nanoscience & nanotechnology ,Computer Science Applications ,Mechanics of Materials ,Modeling and Simulation ,TA401-492 ,Minification ,Noise (video) ,0210 nano-technology ,Transfer of learning ,Phase retrieval ,Algorithm - Abstract
As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.
- Published
- 2021