Image registration of low signal-to-noise cryo-STEM data
• An image registration method optimized for low-SNR, cryogenic STEM data is presented. • Registration errors and subtle artifacts involving unit-cell misalignment are detailed. • Enforcing consistency between alignments of all possible image pairs yields robust image reconstructions. • The importan...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Savitzky, Benjamin H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Who wrote this paper? The ethical issues of authorship - Larsen, Ammon J. ELSEVIER, 2017, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:191 ; year:2018 ; pages:56-65 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ultramic.2018.04.008 |
|---|
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ELV043472052 |
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| 520 | |a • An image registration method optimized for low-SNR, cryogenic STEM data is presented. • Registration errors and subtle artifacts involving unit-cell misalignment are detailed. • Enforcing consistency between alignments of all possible image pairs yields robust image reconstructions. • The importance of Fourier space weighting in low-SNR atomic resolution applications is demonstrated. • All source code is freely available in python, including user-friendly Jupyter notebooks and a GUI. | ||
| 700 | 1 | |a El Baggari, Ismail |4 oth | |
| 700 | 1 | |a Clement, Colin B. |4 oth | |
| 700 | 1 | |a Waite, Emily |4 oth | |
| 700 | 1 | |a Goodge, Berit H. |4 oth | |
| 700 | 1 | |a Baek, David J. |4 oth | |
| 700 | 1 | |a Sheckelton, John P. |4 oth | |
| 700 | 1 | |a Pasco, Christopher |4 oth | |
| 700 | 1 | |a Nair, Hari |4 oth | |
| 700 | 1 | |a Schreiber, Nathaniel J. |4 oth | |
| 700 | 1 | |a Hoffman, Jason |4 oth | |
| 700 | 1 | |a Admasu, Alemayehu S. |4 oth | |
| 700 | 1 | |a Kim, Jaewook |4 oth | |
| 700 | 1 | |a Cheong, Sang-Wook |4 oth | |
| 700 | 1 | |a Bhattacharya, Anand |4 oth | |
| 700 | 1 | |a Schlom, Darrell G. |4 oth | |
| 700 | 1 | |a McQueen, Tyrel M. |4 oth | |
| 700 | 1 | |a Hovden, Robert |4 oth | |
| 700 | 1 | |a Kourkoutis, Lena F. |4 oth | |
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10.1016/j.ultramic.2018.04.008 doi GBV00000000000651.pica (DE-627)ELV043472052 (ELSEVIER)S0304-3991(17)30436-9 DE-627 ger DE-627 rakwb eng 610 VZ 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Savitzky, Benjamin H. verfasserin aut Image registration of low signal-to-noise cryo-STEM data 2018 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • An image registration method optimized for low-SNR, cryogenic STEM data is presented. • Registration errors and subtle artifacts involving unit-cell misalignment are detailed. • Enforcing consistency between alignments of all possible image pairs yields robust image reconstructions. • The importance of Fourier space weighting in low-SNR atomic resolution applications is demonstrated. • All source code is freely available in python, including user-friendly Jupyter notebooks and a GUI. El Baggari, Ismail oth Clement, Colin B. oth Waite, Emily oth Goodge, Berit H. oth Baek, David J. oth Sheckelton, John P. oth Pasco, Christopher oth Nair, Hari oth Schreiber, Nathaniel J. oth Hoffman, Jason oth Admasu, Alemayehu S. oth Kim, Jaewook oth Cheong, Sang-Wook oth Bhattacharya, Anand oth Schlom, Darrell G. oth McQueen, Tyrel M. oth Hovden, Robert oth Kourkoutis, Lena F. oth Enthalten in Elsevier Science Larsen, Ammon J. ELSEVIER Who wrote this paper? The ethical issues of authorship 2017 Amsterdam (DE-627)ELV014927594 volume:191 year:2018 pages:56-65 extent:10 https://doi.org/10.1016/j.ultramic.2018.04.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 191 2018 56-65 10 |
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Image registration of low signal-to-noise cryo-STEM data |
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Image registration of low signal-to-noise cryo-STEM data |
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Savitzky, Benjamin H. |
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image registration of low signal-to-noise cryo-stem data |
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Image registration of low signal-to-noise cryo-STEM data |
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• An image registration method optimized for low-SNR, cryogenic STEM data is presented. • Registration errors and subtle artifacts involving unit-cell misalignment are detailed. • Enforcing consistency between alignments of all possible image pairs yields robust image reconstructions. • The importance of Fourier space weighting in low-SNR atomic resolution applications is demonstrated. • All source code is freely available in python, including user-friendly Jupyter notebooks and a GUI. |
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• An image registration method optimized for low-SNR, cryogenic STEM data is presented. • Registration errors and subtle artifacts involving unit-cell misalignment are detailed. • Enforcing consistency between alignments of all possible image pairs yields robust image reconstructions. • The importance of Fourier space weighting in low-SNR atomic resolution applications is demonstrated. • All source code is freely available in python, including user-friendly Jupyter notebooks and a GUI. |
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• An image registration method optimized for low-SNR, cryogenic STEM data is presented. • Registration errors and subtle artifacts involving unit-cell misalignment are detailed. • Enforcing consistency between alignments of all possible image pairs yields robust image reconstructions. • The importance of Fourier space weighting in low-SNR atomic resolution applications is demonstrated. • All source code is freely available in python, including user-friendly Jupyter notebooks and a GUI. |
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Image registration of low signal-to-noise cryo-STEM data |
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El Baggari, Ismail Clement, Colin B. Waite, Emily Goodge, Berit H. Baek, David J. Sheckelton, John P. Pasco, Christopher Nair, Hari Schreiber, Nathaniel J. Hoffman, Jason Admasu, Alemayehu S. Kim, Jaewook Cheong, Sang-Wook Bhattacharya, Anand Schlom, Darrell G. McQueen, Tyrel M. Hovden, Robert Kourkoutis, Lena F. |
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