High-resolution X-ray diffraction imaging of non-Bragg diffracting materials using phase retrieval X-ray diffractometry (PRXRD) technique

2004 ◽  
Vol 349 (1-4) ◽  
pp. 281-295 ◽  
Author(s):  
A.Y. Nikulin ◽  
A.V. Darahanau ◽  
R. Horney ◽  
T. Ishikawa
Keyword(s):  
High Resolution ◽  
Phase Retrieval ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Imaging

High-resolution coherent x-ray diffraction imaging of metal-coated polymer microspheres

2017 ◽  
Vol 35 (1) ◽  
pp. A7 ◽  
Author(s):  
Eirik T. B. Skjønsfjell ◽  
David Kleiven ◽  
Nilesh Patil ◽  
Yuriy Chushkin ◽  
Federico Zontone ◽  
...  
Keyword(s):  
High Resolution ◽  
Polymer Microspheres ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Imaging

scholarly journals Dark-field phase retrieval under the constraint of the Friedel symmetry in coherent X-ray diffraction imaging

2014 ◽  
Vol 22 (23) ◽  
pp. 27892 ◽  
Author(s):  
Amane Kobayashi ◽  
Yuki Sekiguchi ◽  
Yuki Takayama ◽  
Tomotaka Oroguchi ◽  
Masayoshi Nakasako
Keyword(s):  
Phase Retrieval ◽  
Dark Field ◽  
X Ray Diffraction ◽  
Field Phase ◽  
X Ray ◽  
Diffraction Imaging

High resolution X-ray diffraction imaging of lead tin telluride

1991 ◽  
Vol 114 (4) ◽  
pp. 707-714 ◽  
Author(s):  
Bruce Steiner ◽  
Ronald C. Dobbyn ◽  
David Black ◽  
Harold Burdette ◽  
Masao Kuriyama ◽  
...  
Keyword(s):  
High Resolution ◽  
X Ray Diffraction ◽  
X Ray ◽  
Tin Telluride ◽  
Lead Tin Telluride ◽  
Diffraction Imaging

scholarly journals The influence of strain on image reconstruction in Bragg coherent X-ray diffraction imaging and ptychography

2021 ◽  
Vol 28 (4) ◽  
Author(s):  
Chan Kim ◽  
Markus Scholz ◽  
Anders Madsen
Keyword(s):  
Phase Retrieval ◽  
Superlattice Reflection ◽  
Antiphase Domain ◽  
Phase Changes ◽  
X Ray Diffraction ◽  
X Ray ◽  
Domain Boundaries ◽  
Diffraction Imaging ◽  
Highly Strained ◽  
The Impact

A quantitative analysis of the effect of strain on phase retrieval in Bragg coherent X-ray diffraction imaging is reported. It is shown in reconstruction simulations that the phase maps of objects with strong step-like phase changes are more precisely retrieved than the corresponding modulus values. The simulations suggest that the reconstruction precision for both phase and modulus can be improved by employing a modulus homogenization (MH) constraint. This approach was tested on experimental data from a highly strained Fe–Al crystal which also features antiphase domain boundaries yielding characteristic π phase shifts of the (001) superlattice reflection. The impact of MH is significant and this study outlines a successful method towards imaging of strong phase objects using the next generation of coherent X-ray sources, including X-ray free-electron lasers.

scholarly journals Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Longlong Wu ◽  
Shinjae Yoo ◽  
Ana F. Suzana ◽  
Tadesse A. Assefa ◽  
Jiecheng Diao ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Phase Retrieval ◽  
Three Dimensional ◽  
Current Phase ◽  
X Ray Diffraction ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Model Combining ◽  
Diffraction Imaging ◽  
Improved Performance

AbstractAs 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.

scholarly journals Phase Retrieval by Combined Bragg and Fresnel X-Ray Diffraction Imaging

1998 ◽  
Vol 81 (16) ◽  
pp. 3435-3438 ◽  
Author(s):  
P. Rejmánková-Pernot ◽  
P. Cloetens ◽  
J. Baruchel ◽  
J.-P. Guigay ◽  
P. Moretti
Keyword(s):  
Phase Retrieval ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Imaging

scholarly journals Bonsu: the interactive phase retrieval suite

2012 ◽  
Vol 45 (4) ◽  
pp. 840-843 ◽  
Author(s):  
Marcus C. Newton ◽  
Yoshinori Nishino ◽  
Ian K. Robinson
Keyword(s):  
Phase Retrieval ◽  
Nondestructive Method ◽  
Three Dimensions ◽  
Material Structure ◽  
X Ray Diffraction ◽  
Data Manipulation ◽  
X Ray ◽  
Python Language ◽  
Real Time Visualization ◽  
Diffraction Imaging

Coherent X-ray diffraction imaging has received considerable attention as a nondestructive method for probing material structure at the nanoscale. However, tools for reconstructing and analysing data in both two and three dimensions have lagged somewhat behind.Bonsu, the interactive phase retrieval suite, is the first software package that allows real-time visualization of the reconstruction of phase information in both two and three dimensions. It comes complete with an inventory of algorithms and routines for data manipulation and reconstruction.Bonsuis open source, is designed around the Python language (with C++ bindings) and is largely platform independent.Bonsuis made available under version three of the GNU General Public License and can be found at https://code.google.com/p/bonsu/.

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