Review of single image signal-to-noise ratio estimation for SEM image

Scanning electron microscope (SEM) image signal-to-noise ratio (SNR) depends on the beam current, the materials present in the specimen, and the specimen topography. It is desirable to quantify the SNR in SEM images, as it is a parameter, along with spatial resolution, that quantifies the image qual...

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Main Authors: Sim, K. S., Nia, M. E., Tso, C. P.
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2014
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Online Access:https://hdl.handle.net/10356/101179
http://hdl.handle.net/10220/18594
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1011792023-03-04T17:14:32Z Review of single image signal-to-noise ratio estimation for SEM image Sim, K. S. Nia, M. E. Tso, C. P. School of Mechanical and Aerospace Engineering National Physics Conference (2012) Mechanical and Aerospace Engineering Scanning electron microscope (SEM) image signal-to-noise ratio (SNR) depends on the beam current, the materials present in the specimen, and the specimen topography. It is desirable to quantify the SNR in SEM images, as it is a parameter, along with spatial resolution, that quantifies the image quality. SNR measurement usually requires at least two images, to avoid this requirement, a method of SNR estimation with only a single image is described here. The SNR could be quantified as the ratio of signal variance to noise variance. The autocorrelation of image at its peak (zero offset) is used to estimate the noise variance and the signal component in accordance to the corresponding original autocorrelation and mean of the image while assuming the signal and the noise are uncorrelated. Published version 2014-01-15T06:14:30Z 2019-12-06T20:34:47Z 2014-01-15T06:14:30Z 2019-12-06T20:34:47Z 2013 2013 Journal Article Sim, K. S., Nia, M. E., & Tso, C. P. (2013). Review of single image signal-to-noise ratio estimation for SEM image. AIP Conference Proceedings, 1528, 365-367. 0094-243X https://hdl.handle.net/10356/101179 http://hdl.handle.net/10220/18594 10.1063/1.4803626 en AIP conference proceedings © 2013 AIP Publishing LLC. This paper was published in AIP Conference Proceedings and is made available as an electronic reprint (preprint) with permission of AIP Publishing LLC. The paper can be found at the following official DOI: [http://dx.doi.org/10.1063/1.4803626].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Mechanical and Aerospace Engineering
spellingShingle Mechanical and Aerospace Engineering
Sim, K. S.
Nia, M. E.
Tso, C. P.
Review of single image signal-to-noise ratio estimation for SEM image
description Scanning electron microscope (SEM) image signal-to-noise ratio (SNR) depends on the beam current, the materials present in the specimen, and the specimen topography. It is desirable to quantify the SNR in SEM images, as it is a parameter, along with spatial resolution, that quantifies the image quality. SNR measurement usually requires at least two images, to avoid this requirement, a method of SNR estimation with only a single image is described here. The SNR could be quantified as the ratio of signal variance to noise variance. The autocorrelation of image at its peak (zero offset) is used to estimate the noise variance and the signal component in accordance to the corresponding original autocorrelation and mean of the image while assuming the signal and the noise are uncorrelated.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Sim, K. S.
Nia, M. E.
Tso, C. P.
format Article
author Sim, K. S.
Nia, M. E.
Tso, C. P.
author_sort Sim, K. S.
title Review of single image signal-to-noise ratio estimation for SEM image
title_short Review of single image signal-to-noise ratio estimation for SEM image
title_full Review of single image signal-to-noise ratio estimation for SEM image
title_fullStr Review of single image signal-to-noise ratio estimation for SEM image
title_full_unstemmed Review of single image signal-to-noise ratio estimation for SEM image
title_sort review of single image signal-to-noise ratio estimation for sem image
publishDate 2014
url https://hdl.handle.net/10356/101179
http://hdl.handle.net/10220/18594
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