Difference between revisions of "Projects/SRF: Syncrotron radiation facility deep image retrieval"
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(Created page with "===Project 58 === * Title: X-ray nanotomographyimage reconstruction * Peoblem: To boost quality of nano-, полученных в лабораториях Европейско...") |
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===Project 58 === | ===Project 58 === | ||
* Title: X-ray nanotomographyimage reconstruction | * Title: X-ray nanotomographyimage reconstruction | ||
| − | * Peoblem: To boost quality of nano-, | + | * Peoblem: To boost quality of nano-object images, obtained in the European Syncrotron Radiation Facility, [https://www.osug.fr/IMG/pdf/zontone_osug_workshop2017id10.pdf ESRF]. |
| − | + | * Data: on your request, 3GB | |
* References: | * References: | ||
** [https://arxiv.org/pdf/1809.04626.pdf] Iterative phase retrieval in coherent diffractive imaging: practical issues | ** [https://arxiv.org/pdf/1809.04626.pdf] Iterative phase retrieval in coherent diffractive imaging: practical issues | ||
Revision as of 19:47, 20 August 2020
Project 58
- Title: X-ray nanotomographyimage reconstruction
- Peoblem: To boost quality of nano-object images, obtained in the European Syncrotron Radiation Facility, ESRF.
- Data: on your request, 3GB
- References:
- [1] Iterative phase retrieval in coherent diffractive imaging: practical issues
- [2] X-ray nanotomography of coccolithophores reveals that coccolith mass and segment number correlate with grid size
- [3] Lens-free microscopy for 3D + time acquisitions of 3D cell culture
- [4] DEEP ITERATIVE RECONSTRUCTION FOR PHASE RETRIEVAL
- LinkReview
- AUSPEX is a diagnostic tool for graphical X-Ray data analysis, see common pathologies and their causes
- Basic: Gerchberg-Saxton algorithm
- Method: To boost Gerchberg-Saxton with neural networks. Use Bayesian approach and set physical models as expert-given prior information
- Novelty: we are developing expert learning method
- Authors: Sergei Grudinin, Yuri Chushkin, and Vadim Strijov