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The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images.

Publication ,  Journal Article
Stevens, A; Yang, H; Carin, L; Arslan, I; Browning, ND
Published in: Microscopy (Oxford, England)
February 2014

The use of high-resolution imaging methods in scanning transmission electron microscopy (STEM) is limited in many cases by the sensitivity of the sample to the beam and the onset of electron beam damage (for example, in the study of organic systems, in tomography and during in situ experiments). To demonstrate that alternative strategies for image acquisition can help alleviate this beam damage issue, here we apply compressive sensing via Bayesian dictionary learning to high-resolution STEM images. These computational algorithms have been applied to a set of images with a reduced number of sampled pixels in the image. For a reduction in the number of pixels down to 5% of the original image, the algorithms can recover the original image from the reduced data set. We show that this approach is valid for both atomic-resolution images and nanometer-resolution studies, such as those that might be used in tomography datasets, by applying the method to images of strontium titanate and zeolites. As STEM images are acquired pixel by pixel while the beam is scanned over the surface of the sample, these postacquisition manipulations of the images can, in principle, be directly implemented as a low-dose acquisition method with no change in the electron optics or the alignment of the microscope itself.

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Published In

Microscopy (Oxford, England)

DOI

EISSN

2050-5701

ISSN

2050-5698

Publication Date

February 2014

Volume

63

Issue

1

Start / End Page

41 / 51
 

Citation

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Stevens, A., Yang, H., Carin, L., Arslan, I., & Browning, N. D. (2014). The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images. Microscopy (Oxford, England), 63(1), 41–51. https://doi.org/10.1093/jmicro/dft042
Stevens, Andrew, Hao Yang, Lawrence Carin, Ilke Arslan, and Nigel D. Browning. “The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images.Microscopy (Oxford, England) 63, no. 1 (February 2014): 41–51. https://doi.org/10.1093/jmicro/dft042.
Stevens A, Yang H, Carin L, Arslan I, Browning ND. The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images. Microscopy (Oxford, England). 2014 Feb;63(1):41–51.
Stevens, Andrew, et al. “The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images.Microscopy (Oxford, England), vol. 63, no. 1, Feb. 2014, pp. 41–51. Epmc, doi:10.1093/jmicro/dft042.
Stevens A, Yang H, Carin L, Arslan I, Browning ND. The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images. Microscopy (Oxford, England). 2014 Feb;63(1):41–51.
Journal cover image

Published In

Microscopy (Oxford, England)

DOI

EISSN

2050-5701

ISSN

2050-5698

Publication Date

February 2014

Volume

63

Issue

1

Start / End Page

41 / 51