Machine Learning for Sub-pixel Super-resolution in Direct Electron Detectors
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Abstract
Gabriela Correa, David Muller; Machine Learning for Sub-pixel Super-resolution in Direct Electron Detectors, Microscopy and Microanalysis, Volume 26, Issue
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Source: openalex_inverted_index · Quality: short
Research context
OpenAlex topics: Particle Detector Development and Performance · Radiation Detection and Scintillator Technologies · CCD and CMOS Imaging Sensors
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References (5)
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3. 10.1017/S143192761501566410.1017/S1431927615015664 ↗
4. 10.1016/j.nima.2008.09.02910.1016/j.nima.2008.09.029 ↗
5. [5] This work used resources provided by the National Science Foundation Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials (PARADIM) under Cooperative Agreement No. NSF-DMR-1539918, and resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. GCC acknowledges support by the Alfred P. Sloan Foundation and Department of Energy Computational Science Graduate Fellowship (DOE CSGF), which is provided under grant number DE-FG02-97ER25308.