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Title: Noise variance estimation using image noise cross-correlation model on SEM images
Authors: Sim, K. S.
Nia, M. E.
Tso, Chih Ping.
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2012
Source: Sim, K. S., Nia, M. E.,& Tso, C. P. (2013). Noise variance estimation using image noise cross-correlation model on SEM images. Scanning, 35(3), 205-212.
Series/Report no.: Scanning
Abstract: A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.
ISSN: 0161-0457
DOI: 10.1002/sca.21055
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2012 Wiley Periodicals, Inc.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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