scholarly journals Sparse Phase Retrieval Algorithm for Observing Isolated Magnetic Skyrmions by Coherent Soft X-ray Diffraction Imaging

2019 ◽  
Vol 88 (2) ◽  
pp. 024009 ◽  
Author(s):  
Yuichi Yokoyama ◽  
Taka-hisa Arima ◽  
Masato Okada ◽  
Yuichi Yamasaki
Keyword(s):  
Phase Retrieval ◽  
Retrieval Algorithm ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Imaging ◽  
Magnetic Skyrmions ◽  
Phase Retrieval Algorithm

Near-field x-ray phase contrast imaging and phase retrieval algorithm

2005 ◽  
Vol 14 (4) ◽  
pp. 796-801 ◽  
Author(s):  
Zhu Hua-Feng ◽  
Xie Hong-Lan ◽  
Gao Hong-Yi ◽  
Chen Jian-Wen ◽  
Li Ru-Xin ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Phase Retrieval ◽  
Near Field ◽  
Phase Contrast Imaging ◽  
Retrieval Algorithm ◽  
Contrast Imaging ◽  
X Ray ◽  
Phase Retrieval Algorithm

A phase retrieval algorithm for polychromatic x-ray in-line phase contrast imaging

2012 ◽  
Author(s):  
Xue-jun Guo ◽  
Xiao-lin Liu ◽  
Mu Gu ◽  
Chen Ni ◽  
Bo Liu ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Phase Retrieval ◽  
Phase Contrast Imaging ◽  
Retrieval Algorithm ◽  
Contrast Imaging ◽  
X Ray ◽  
Phase Retrieval Algorithm

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

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 Impact of a single distance phase retrieval algorithm on spatial resolution in X-ray inline phase sensitive imaging

2019 ◽  
Vol 8 (1-2) ◽  
pp. 29-40 ◽  
Author(s):  
Muhammad U. Ghani ◽  
Bradley Gregory ◽  
Farid Omoumi ◽  
Bin Zheng ◽  
Aimin Yan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Phase Retrieval ◽  
Retrieval Algorithm ◽  
X Ray ◽  
Phase Sensitive ◽  
Phase Retrieval Algorithm
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