Citation

Sim, Kok Swee and Norhisham, Syafiq (2016) Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation. Journal of Microscopy, 264 (2). pp. 159-174. ISSN 0022-2720; eISSN: 1365-2818

Full text not available from this repository.

Abstract

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods.

Item Type:	Article
Uncontrolled Keywords:	Autocorrelation function; Gaussian noise; SNR estimation; image analysis; nonlinear least squares regression; scanning electron microscope
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	12 Dec 2017 16:35
Last Modified:	12 Dec 2017 16:36
URII:	http://shdl.mmu.edu.my/id/eprint/6617

Downloads

Edit (login required)