دورية أكاديمية
Complex imaging of phase domains by deep neural networks
| العنوان: | Complex imaging of phase domains by deep neural networks |
|---|---|
| المؤلفون: | Longlong Wu, Pavol Juhas, Shinjae Yoo, Ian Robinson |
| المصدر: | IUCrJ, Vol 8, Iss 1, Pp 12-21 (2021) |
| بيانات النشر: | International Union of Crystallography, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Crystallography |
| مصطلحات موضوعية: | machine learning, bragg coherent x-ray diffraction, phase retrieval, single-particle imaging, deep neural networks, Crystallography, QD901-999 |
| الوصف: | The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2052-2525 20522525 |
| Relation: | http://scripts.iucr.org/cgi-bin/paper?S2052252520013780; https://doaj.org/toc/2052-2525 |
| DOI: | 10.1107/S2052252520013780 |
| URL الوصول: | https://doaj.org/article/67cd66d7a88d4e06a69f142fdfa40ad5 |
| رقم الأكسشن: | edsdoj.67cd66d7a88d4e06a69f142fdfa40ad5 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20522525 |
|---|---|
| DOI: | 10.1107/S2052252520013780 |