Multi-Resolution Data Fusion for Super-Resolution Electron Microscopy

Suhas Sreehari, S. V. Venkatakrishnan, Katherine L. Bouman, Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 88-96


Perhaps surprisingly, all electron microscopy (EM) data collected to date is less than a cubic millimeter -- presenting a huge demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution. Traditional EM imaging based on homogeneous raster scanning severely limits the volume of high-resolution data that can be collected, and presents a fundamental limitation to understanding physical processes such as material deformation and crack propagation. We introduce a multi-resolution data fusion (MDF) method for super-resolution computational EM. Our method combines innovative data acquisition with novel algorithmic techniques to dramatically improve the resolution/volume/speed trade-off. The key to our approach is to collect the entire sample at low resolution, while simultaneously collecting a small fraction of data at high resolution. The high-resolution measurements are then used to create a material-specific model that is used within the "plug-and-play" framework to dramatically improve resolution of the low-resolution data. We present results using FEI electron microscope data that demonstrate super-resolution factors of 4x-16x, while substantially maintaining high image quality and reducing dosage.

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[pdf] [arXiv]
author = {Sreehari, Suhas and Venkatakrishnan, S. V. and Bouman, Katherine L. and Simmons, Jeffrey P. and Drummy, Lawrence F. and Bouman, Charles A.},
title = {Multi-Resolution Data Fusion for Super-Resolution Electron Microscopy},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}